{"id":48,"date":"2021-11-30T09:00:00","date_gmt":"2021-11-30T01:00:00","guid":{"rendered":""},"modified":"2026-10-13T17:33:17","modified_gmt":"2026-10-13T09:33:17","slug":"drkevinzhang-cn","status":"publish","type":"post","link":"http:\/\/www.nlpir.org\/wordpress\/2021\/11\/30\/drkevinzhang-cn\/","title":{"rendered":"\u5f20\u534e\u5e73"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"48\" class=\"elementor elementor-48\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6e80b69e e-flex e-con-boxed e-con e-parent\" data-id=\"6e80b69e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-275af3b7 elementor-widget elementor-widget-text-editor\" data-id=\"275af3b7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><img decoding=\"async\" class=\"alignnone wp-image-9252\" src=\"http:\/\/www.nlpir.org\/wordpress\/wp-content\/uploads\/2025\/05\/640.webp\" alt=\"\" width=\"185\" height=\"252\"><\/p>\n<p><a href=\"http:\/\/www.nlpir.org\/wordpress\/2018\/12\/12\/dr-kevin-zhang-en\/\">CV in English Version<\/a><\/p>\n<div>\n<div>\n<div><span style=\"font-family: verdana;\">\u5f20\u534e\u5e73 \u7279\u8058\u6559\u6388&nbsp; \u535a\u58eb\u751f\u5bfc\u5e08 \uff08\u6bcf\u5e74\u62df\u62db4\u4e2a\u535a\u58eb\uff0c6\u4e2a\u7855\u58eb\uff0c2\u4e2a\u535a\u540e\uff0c2\u540d\u9752\u5e74\u4eba\u624d\uff0c\u6b22\u8fce\u62a5\u8003\uff09<\/span><\/div>\n<div>\u56fd\u5bb6\u7ea7\u9886\u519b\u4eba\u624d<\/div>\n<div><span style=\"font-family: verdana;\">\u5317\u7406\u5de5\u4eba\u5de5\u667a\u80fd\u5b66\u9662&amp;\u8ba1\u7b97\u673a\u5b66\u9662\u526f\u9662\u957f\uff0c\u6570\u5b57\u5316\u4e0e\u667a\u7b97\u6280\u672f\u4e2d\u5fc3\u526f\u4e3b\u4efb\uff1bNLPIR\u5b9e\u9a8c\u5ba4\u4e3b\u4efb<\/span><\/div>\n<div><span style=\"font-family: verdana;\">\u5730\u5740\uff1a\u5317\u4eac\u6d77\u6dc0\u533a\u4e2d\u5173\u6751\u5357\u5927\u88575\u53f7 100081<\/span><\/div>\n<div><span style=\"font-family: verdana;\">Email:kevinzhang@bit.edu.cn<\/span><\/div>\n<div><span style=\"font-family: verdana;\">MSN: &nbsp;pipy_zhang@msn.com;<\/span><\/div>\n<div><span style=\"font-family: verdana;\">\u7f51\u7ad9: http:\/\/www.nlpir.org (\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u4fe1\u606f\u68c0\u7d22\u5171\u4eab\u5e73\u53f0)<\/span><\/div>\n<div><span style=\"font-family: verdana;\">\u5fae\u535a:http:\/\/www.weibo.com\/drkevinzhang\/<\/span><\/div>\n<div>\u5fae\u4fe1\u516c\u4f17\u53f7\uff1a\u5927\u6570\u636e\u5343\u4eba\u4f1a<\/div>\n<div>\n<div><span style=\"font-family: verdana;\">GitHub\uff1a<\/span><span style=\"font-family: verdana;\">https:\/\/github.com\/NLPIR-team\/NLPIR<\/span><\/div>\n<div><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter size-full wp-image-2910611\" src=\"http:\/\/www.nlpir.org\/wordpress\/wp-content\/uploads\/2026\/07\/image.jpeg\" alt=\"\" width=\"900\" height=\"1200\" srcset=\"http:\/\/www.nlpir.org\/wordpress\/wp-content\/uploads\/2026\/07\/image.jpeg 900w, http:\/\/www.nlpir.org\/wordpress\/wp-content\/uploads\/2026\/07\/image-225x300.jpeg 225w, http:\/\/www.nlpir.org\/wordpress\/wp-content\/uploads\/2026\/07\/image-768x1024.jpeg 768w, http:\/\/www.nlpir.org\/wordpress\/wp-content\/uploads\/2026\/07\/image-1152x1536.jpeg 1152w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2><strong>\u57fa\u672c\u60c5\u51b5\uff1a<\/strong><\/h2>\n<p>\u5f20\u534e\u5e73\uff0c\u5317\u4eac\u7406\u5de5\u5927\u5b66\u7279\u8058\u6559\u6388\uff0c\u56fd\u5bb6\u7ea7\u9886\u519b\u4eba\u624d\uff08\u6559\u80b2\u90e8\u4eba\u624d\u91cd\u5927\u5de5\u7a0b\u83b7\u5f97\u8005\uff09\uff0c\u535a\u58eb\u751f\u5bfc\u5e08\uff0c\u4eba\u5de5\u667a\u80fd\u5b66\u9662&amp;\u8ba1\u7b97\u673a\u5b66\u9662\u526f\u9662\u957f\uff0c\u6570\u5b57\u5316\u4e0e\u667a\u7b97\u6280\u672f\u4e2d\u5fc3\u526f\u4e3b\u4efb\uff0c\u5168\u56fd\u5de5\u4e1a\u548c\u4fe1\u606f\u5316\u7cfb\u7edf\u5148\u8fdb\u5de5\u4f5c\u8005\uff0c\u65b0\u7586\u5929\u6c60\u82f1\u624d\uff0cNLPIR\u5b9e\u9a8c\u5ba4\u4e3b\u4efb\uff0c\u77e5\u540d\u6c49\u8bed\u5206\u8bcd\u7cfb\u7edfICTCLAS\u521b\u59cb\u4eba\uff0c\u4e2d\u56fd\u4eba\u5de5\u667a\u80fd\u5b66\u4f1a\u7406\u4e8b\uff0c<a href=\"http:\/\/www.imlip.org\">\u591a\u8bed\u79cd\u667a\u80fd\u4fe1\u606f\u5904\u7406\u4e13\u4e1a\u59d4\u5458\u4f1a<\/a>\u4e3b\u4efb\uff0c\u4e2d\u56fd\u8ba1\u7b97\u673a\u5b66\u4f1a\u6770\u51fa\u4f1a\u5458\uff0c<a href=\"https:\/\/baike.baidu.com\/item\/%E4%BF%A1%E6%81%AF%E7%A4%BE%E4%BC%9A50%E4%BA%BA%E8%AE%BA%E5%9D%9B\/19191893\">\u4fe1\u606f\u793e\u4f1a50\u4eba\u8bba\u575b<\/a>\u6210\u5458\uff0c\u4e2d\u5173\u6751\u5b9e\u9a8c\u5ba4\u53cc\u8058\u6559\u6388\uff0c\u65b0\u7586\u5927\u5b66\u517c\u804c\u535a\u5bfc\uff1b\u300aJournal of Big Data\u300b\u526f\u4e3b\u7f16\uff0c\u300aData Intelligence\u300b\u300a\u667a\u80fd\u7cfb\u7edf\u5b66\u62a5\u300b\u300a\u4eba\u5de5\u667a\u80fd\u300b\u300a\u5e94\u7528\u79d1\u6280\u300b\u7f16\u59d4\uff1b\u6559\u80b2\u90e8\u5b66\u4f4d\u4e2d\u5fc3\u535a\u58eb\u5b66\u4f4d\u8bba\u6587\u3001\u591a\u4e2a\u9876\u4f1a\u9876\u520a\u8bc4\u5ba1\u4e13\u5bb6\u3002\u7814\u7a76\u65b9\u5411\u4e3a\uff1a\u591a\u8bed\u79cd\u667a\u80fd\u4fe1\u606f\u5904\u7406\u3001\u5927\u6570\u636e\u641c\u7d22\u4e0e\u6316\u6398\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u60c5\u62a5\u6316\u6398\u5206\u6790\u3002\u5f20\u534e\u5e73\u4f5c\u4e3a\u8bfe\u9898\u7ec4\u957f\u4e3b\u6301\u5f00\u53d1\u4e86\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u3001\u521b\u65b0\u7279\u533a\u8bfe\u9898\u3001\u57fa\u7840\u52a0\u5f3a\u91cd\u70b9\u8bfe\u9898\u7b49\u56fd\u5bb6\u7ea7\u79d1\u7814\u8bfe\u9898\u51e0\u5341\u4f59\u9879\uff0c\u7275\u5934\u83b7\u5f97\u56fd\u5bb6\u79d1\u6280\u8fdb\u6b65\u5956\u4e8c\u7b49\u5956\uff08\u5e8f1\uff09\u3001\u56fd\u5bb6\u4fdd\u5bc6\u79d1\u6280\u5956\u4e00\u7b49\u5956\uff08\u5e8f1\uff09\uff0c\u56fd\u9632\u79d1\u6280\u8fdb\u6b65\u5956\u4e8c\u7b49\u59562\u9879\uff08\u5e8f1\uff09\u5728\u5185\u7684\u79d1\u6280\u59567\u9879\u3002\u51fa\u7248\u300a\u5927\u6570\u636e\u667a\u80fd\u5206\u6790\u300b\u3001\u300a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u5e94\u7528\u300b\u7b49\u4e13\u84576\u90e8\u3002\u53d1\u8868ACL\u3001ICML\u3001IJCAI\u3001KDD\u7b49\u9876\u7ea7\u8bba\u6587\u767e\u4f59\u7bc7\u3002\u6240\u7814\u5236\u7684NLPIR\/ICTCLAS\u8bed\u4e49\u5206\u6790\u7cfb\u7edf\u662f\u4e2d\u6587\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u5f15\u7528\u6700\u591a\u7684\u6210\u679c\uff08\u56fd\u5185\u5916\u5f15\u7528\u8fd13\u4e07\u6b21\uff09\u3002<\/p>\n<h2><strong>\u5b66\u672f\u4efb\u804c\uff1a<\/strong><\/h2>\n<p>\u4e2d\u56fd\u4eba\u5de5\u667a\u80fd\u5b66\u4f1a<a href=\"http:\/\/www.imlip.org\">\u591a\u8bed\u79cd\u667a\u80fd\u4fe1\u606f\u5904\u7406\u4e13\u4e1a\u59d4\u5458\u4f1a<\/a>\u79d8\u4e66\u957f<\/p>\n<p><a href=\"https:\/\/baike.baidu.com\/item\/%E4%BF%A1%E6%81%AF%E7%A4%BE%E4%BC%9A50%E4%BA%BA%E8%AE%BA%E5%9D%9B\/19191893\">\u4fe1\u606f\u793e\u4f1a50\u4eba\u8bba\u575b<\/a>\u6210\u5458<\/p>\n<p>\u4e2d\u56fd\u8ba1\u7b97\u673a\u5b66\u4f1a\u6770\u51fa\u4f1a\u5458<\/p>\n<p>\u5c71\u4e1c\u5927\u5b66\u620e\u667a\u519b\u4e8b\u667a\u80fd\u4e0e\u4eff\u771f\u5b9e\u9a8c\u5ba4\u4e13\u5bb6\u59d4\u5458\u4f1a\u59d4\u5458<\/p>\n<p>\u6c5f\u897f\u5e08\u5927\u8bed\u8a00\u7a7a\u95f4\u4fe1\u606f\u79d1\u5b66\u7814\u7a76\u4e2d\u5fc3\u5b66\u672f\u59d4\u5458<\/p>\n<p>\u5168\u56fd\u793e\u4f1a\u8206\u60c5\u5206\u6790\u8bba\u575b\u4e3b\u5e2d<\/p>\n<p>\u8fbd\u5b81\u5e08\u8303\u5927\u5b66\u5ba2\u5ea7\u6559\u6388<\/p>\n<p>\u9996\u90fd\u5e08\u8303\u5927\u5b66\u517c\u804c\u5bfc\u5e08<\/p>\n<p>\u65b0\u7586\u5927\u5b66\u517c\u804c\u7814\u7a76\u751f\u5bfc\u5e08<\/p>\n<p>\u4e2d\u56fd\u8ba1\u7b97\u673a\u5b66\u4f1a\u9752\u5e74\u79d1\u6280\u8bba\u575bYOCSEF\u59d4\u5458<\/p>\n<p>\u4e2d\u56fd\u8ba1\u7b97\u673a\u5b66\u4f1a\u666e\u53ca\u5de5\u59d4\u59d4\u5458<\/p>\n<p>\u56fd\u5bb6\u7ea7\u4eba\u624d\u8bc4\u5ba1\u4e13\u5bb6<\/p>\n<p>\u5317\u4eac\u5e02\u91cd\u70b9\u4ea7\u4e1a\u77e5\u8bc6\u4ea7\u6743\u8054\u76df\u4e13\u5bb6<\/p>\n<p>\u5317\u4eac\u5e02\u987a\u4e49\u533a\u653f\u5e9c\u4e13\u5bb6\u54a8\u8be2\u59d4\u5458\u4f1a\u59d4\u5458<\/p>\n<h2>\u6559\u80b2\u7ecf\u5386\uff1a<\/h2>\n<p>1. 2007.11-2008.5 \u5317\u4eac\u5927\u5b66\u5149\u534e\u7ba1\u7406\u5b66\u9662\u9ad8\u7ea7\u5de5\u5546\u7ba1\u7406\u7855\u58eb\u8fdb\u4fee\u73ed6\u671f\u7ed3\u4e1a<\/p>\n<p>2. 2000.9-2005.7 \u4e2d\u56fd\u79d1\u5b66\u9662\u8ba1\u7b97\u6280\u672f\u7814\u7a76\u6240 \u8ba1\u7b97\u673a\u8f6f\u4ef6\u4e0e\u7406\u8bba \u535a\u58eb\u5b66\u4f4d\uff08\u7855\u535a\u8fde\u8bfb\uff09<\/p>\n<h2><strong>\u5de5\u4f5c\u7ecf\u5386\uff1a<\/strong><\/h2>\n<p>1. 2009.11-\u81f3\u4eca\uff0c\u5317\u4eac\u7406\u5de5\u5927\u5b66\u8ba1\u7b97\u673a\u5b66\u9662\u526f\u9662\u957f\uff0c\u4eba\u5de5\u667a\u80fd\u5b66\u9662\u526f\u9662\u957f\uff0c\u667a\u7b97\u4e2d\u5fc3\u526f\u4e3b\u4efb\uff0c NLPIR\u5b9e\u9a8c\u5ba4\u4e3b\u4efb\uff0c\u526f\u7814\u7a76\u5458\uff0c\u7279\u8058\u7814\u7a76\u5458\uff0c\u6559\u6388\uff0c\u7279\u8058\u6559\u6388\uff0c\u535a\u58eb\u751f\u5bfc\u5e08\uff1b<\/p>\n<p>2. 2006.9-2009.11\uff0c\u4e2d\u56fd\u79d1\u5b66\u9662\u8ba1\u7b97\u6280\u672f\u7814\u7a76\u6240\u526f\u7814\u7a76\u5458\uff1b<\/p>\n<p>3. 2005.7-2006.9\u52a9\u7406\u7814\u7a76\u5458\uff0c\u4e2d\u56fd\u79d1\u5b66\u9662\u8ba1\u7b97\u6280\u672f\u7814\u7a76\u6240\u6d45\u5c42\u8bed\u8a00\u5904\u7406\u8bfe\u9898\u7ec4\u7ec4\u957f\uff1b<\/p>\n<h2><strong>\u4e3b\u8981\u83b7\u5956\u60c5\u51b5\uff1a<\/strong><\/h2>\n<p>2025\u5e74\uff0c\u98ce\u9669\u667a\u80fd\u8bc6\u522b\u5904\u7f6e\u5173\u952e\u6280\u672f\u4e0e\u88c5\u5907\uff0c\u56fd\u5bb6\u79d1\u6280\u8fdb\u6b65\u5956\u4e8c\u7b49\u5956\uff08\u5e8f1\uff09<\/p>\n<p>2025\u5e74\uff0c\u83b7\u5168\u56fd\u5de5\u4e1a\u548c\u4fe1\u606f\u5316\u7cfb\u7edf\u5148\u8fdb\u5de5\u4f5c\u8005<\/p>\n<p>2024\u5e74\uff0c\u9762\u5411*\u7684\u6587\u56fe\u97f3\u50cf\u591a\u6a21\u6001\u667a\u80fd\u8bc6\u522b\u5173\u952e\u6280\u672f\u53ca\u5e94\u7528, \u56fd\u5bb6**\u79d1\u5b66\u6280\u672f\u5956\u4e00\u7b49\u5956\uff08\u5e8f1\uff09<\/p>\n<p>2023\u5e74\uff0c\u83b7\u56fd\u5bb6\u7ea7\u9886\u519b\u4eba\u5956\u52b1\u8ba1\u5212\u79f0\u53f7<\/p>\n<p>2023\u5e74\uff0c\u7f51\u7edc\u7a7a\u95f4\u591a\u8bed\u79cd\u5185\u5bb9*\u5173\u952e\u6280\u672f\u53ca\u5e94\u7528, \u56fd\u9632\u79d1\u6280\u8fdb\u6b65\u5956\u4e8c\u7b49\u5956\uff08\u5e8f1\uff09<\/p>\n<p>2022\u5e74\uff0c\u5927\u6570\u636e**\u8ba4\u77e5\u4e0e\u751f\u6210\u5173\u952e\u6280\u672f\u53ca\u5e94\u7528, \u56fd\u9632\u79d1\u6280\u8fdb\u6b65\u5956\u4e8c\u7b49\u5956\uff08\u5e8f1\uff09<\/p>\n<p>2021\u5e74\uff0c\u6c49\u7ef4\u5927\u6570\u636e\u8bed\u4e49\u667a\u80fd\u5206\u6790\u5173\u952e\u6280\u672f\u53ca\u5e94\u7528, \u65b0\u7586\u81ea\u6cbb\u533a\u653f\u5e9c\u79d1\u6280\u8fdb\u6b65\u5956\u4e00\u7b49\u5956\uff08\u5e8f2\uff09<\/p>\n<p>2010\u5e74\u83b7\u5f97\u94b1\u4f1f\u957f\u4e2d\u6587\u4fe1\u606f\u5904\u7406\u79d1\u5b66\u6280\u672f\u5956\u4e00\u7b49\u5956\uff08\u4e2d\u6587\u4fe1\u606f\u9886\u57df\u6700\u9ad8\u5956\uff09\uff08\u5e8f2\uff09<\/p>\n<p>2016\u5e74\uff0c\u4e2d\u592e\u7f51\u4fe1\u529e\u5341\u4f73\u8bb2\u5e08<\/p>\n<p>2007\u5e74\u8ba1\u7b97\u6240\u201c\u767e\u661f\u8ba1\u5212\u201d\u9996\u6279\u5165\u9009\u8005(1%)<\/p>\n<p>2008\u5e74\u83b7\u8ba1\u7b97\u6240\u4f18\u79c0\u4e2a\u4eba(1%)<\/p>\n<p>2023\u5e74\uff0c\u5317\u4eac\u7406\u5de5\u5927\u5b66\u201c\u7559\u5b66\u5317\u7406\u201d\u6211\u6700\u559c\u7231\u7684\u8001\u5e08\u79f0\u53f7\uff1b\u5317\u4eac\u7406\u5de5\u5927\u5b66\u4f18\u79c0\u5171\u4ea7\u515a\u5458\u79f0\u53f7<\/p>\n<p>2022\u5e74\uff0c\u5317\u4eac\u7406\u5de5\u5927\u5b66\u4f18\u79c0\u201c\u4e09\u5168\u80b2\u4eba\u201c\u5bfc\u5e08<\/p>\n<p>2021\u5e74\uff0c\u300a\u5927\u6570\u636e\u667a\u80fd\u5206\u6790\u300b\u83b7\u5f97\u5317\u4eac\u7406\u5de5\u5927\u5b66\u4f18\u79c0\u7814\u7a76\u751f\u6559\u6750\uff0c2022\u5e74\u5ea6\u5317\u4eac\u5e02\u4f18\u79c0\u672c\u79d1\u751f\u6559\u6750<\/p>\n<p><strong>\u7814\u7a76\u751f\u671f\u95f4\uff1a<\/strong><\/p>\n<p>2004\u5e74\u83b7\u8ba1\u7b97\u6240\u6240\u957f\u7279\u522b\u5956(0.5%)<br>2004\u5e74\u83b7\u4e2d\u79d1\u9662\u9662\u957f\u5956(0.2%)<br>2006\u5e74\u83b7\u8ba1\u7b97\u6240\u4f18\u79c0\u4e2a\u4eba(1%)<br>2007\u5e74\u83b7\u8ba1\u7b97\u6240\u4f18\u79c0\u56e2\u961f(1%)<\/p>\n<h2><strong>\u65b0\u95fb\u5a92\u4f53\u62a5\u9053\uff1a<\/strong><\/h2>\n<p id=\"activity-name\" class=\"rich_media_title \"><a href=\"https:\/\/mp.weixin.qq.com\/s\/bZhuLDDElViPHI66ZlJmBw\">\u5148\u8fdb\uff01\u4eca\u5929\uff0c\u4ed6\u4eec\u83b7\u8868\u5f70\uff01<\/a><br><a href=\"https:\/\/mp.weixin.qq.com\/s\/OPf8c4306c89-eQnCTxDuw\">\u300a\u4eba\u6c11\u65e5\u62a5\u300b\u62a5\u9053\u5317\u7406\u5de5\u201c\u667a\u6167\u601d\u653f\u201d\uff0c\u79d1\u6280\u611f\u6d53\u6d53\uff01<\/a><\/p>\n<p id=\"activity-name\" class=\"rich_media_title \"><a href=\"https:\/\/mp.weixin.qq.com\/s\/zk9QyzIMJim03CFoDVXMZw\">\u5f20\u534e\u5e73\uff1a\u8eac\u8015\u6559\u575b\uff0c\u57f9\u517b\u9886\u519b\u4eba\u624d<\/a><\/p>\n<h2><strong>\u4ee3\u8868\u6027\u8bba\u6587\uff1a<\/strong><\/h2>\n<p>Wenyao Cui, Huaping Zhang; SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification; ACM KDD2026, &nbsp;Jeju, Korea August 9-13,&nbsp;<em>2026<\/em><\/p>\n<p>Yunhan Bu, Huaping Zhang; Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization, ICML 2026, Seoul, Korea July 6\u201311, <em>2026<\/em><\/p>\n<p>Haocheng Lv, Huaping Zhang; Improving Multimodal Reasoning via Worst Dimension Optimization,IJCAI-ECAI 2026, the 35th International Joint Conference on Artificial Intelligence, Bremen, Germany, on August 15-21, 2026<\/p>\n<h3><a title=\"\" href=\"https:\/\/openreview.net\/profile?id=~Yinuo_Wang16\" data-toggle=\"tooltip\" data-placement=\"top\" data-original-title=\"~Yinuo_Wang16\">Yinuo Wang<\/a>,&nbsp;<a title=\"\" href=\"https:\/\/openreview.net\/profile?id=~Qingjie_Li1\" data-toggle=\"tooltip\" data-placement=\"top\" data-original-title=\"~Qingjie_Li1\">Qingjie Li<\/a>,&nbsp;<a title=\"\" href=\"https:\/\/openreview.net\/profile?id=~Wenyao_Cui1\" data-toggle=\"tooltip\" data-placement=\"top\" data-original-title=\"~Wenyao_Cui1\">Wenyao Cui<\/a>,&nbsp;<a title=\"\" href=\"https:\/\/openreview.net\/profile?id=~Qiuchi_Li1\" data-toggle=\"tooltip\" data-placement=\"top\" data-original-title=\"~Qiuchi_Li1\">Qiuchi Li<\/a>,&nbsp;<a title=\"\" href=\"https:\/\/openreview.net\/profile?id=~Zhang_Huaping1\" data-toggle=\"tooltip\" data-placement=\"top\" data-original-title=\"~Zhang_Huaping1\">Zhang Huaping<\/a>&nbsp;,2026.ConMA : Confidence-Guided Kernel Sampling with Multi-Stage Aggregation for LLM Reasoning,<span id=\"citeACL\">In&nbsp;<i>Findings of the Association for Computational Linguistics ACL 2026<\/i>, . Association for Computational Linguistics.<\/span><button class=\"btn btn-clipboard btn-secondary btn-sm ml-2\" type=\"button\" data-clipboard-target=\"#citeACL\"><i class=\"far fa-clipboard\"><\/i><\/button> (CCF A top conference)<\/h3>\n<p>Yelin Chen, Huaping Zhang, Ruohao Yan, Jihong Zhu, Askar Hamdulla,LiteNER: A novel lightweight method for long text named entity recognition,Information Processing &amp; Management,Volume 64, Issue 1, Part A,2027,104981,ISSN 0306-4573,https:\/\/doi.org\/10.1016\/j.ipm.2026.104981.<br>(https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306457326003729)<\/p>\n<p>Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li ,2026.BCL: Bayesian In-Context Learning Framework for Information Extraction,<span id=\"citeACL\">In&nbsp;<i>Findings of the Association for Computational Linguistics ACL 2026<\/i>, . Association for Computational Linguistics.<\/span><button class=\"btn btn-clipboard btn-secondary btn-sm ml-2\" type=\"button\" data-clipboard-target=\"#citeACL\"><i class=\"far fa-clipboard\"><\/i><\/button> (CCF A top conference)<\/p>\n<p>Sen Jia, Huayu Wang, Hsiang-Wei Huang, Zhaochong An, Jenq-Neng Hwang, Zhang Huaping, Lei Li ,2026\uff0cCLEP: Contrastive Language-Pose Pretraining<br>CVPR2026<\/p>\n<p>Sen Jia, Ning Zhu, Jinqin Zhong, Jiale Zhou, Zhang Huaping, Jenq-Neng Hwang, Lei Li ,2026,RAM: Recover Any 3D Human Motion in-the-Wild<br>,CVPR2026<\/p>\n<p><span id=\"citeACL\">Linhan Li and Zhang Huaping. 2024.&nbsp;<a href=\"https:\/\/aclanthology.org\/2024.findings-acl.249\">Context Length Extension via Generalized Extrapolation Scale<\/a>. In&nbsp;<i>Findings of the Association for Computational Linguistics ACL 2024<\/i>, pages 4211\u20134218, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.<\/span><button class=\"btn btn-clipboard btn-secondary btn-sm ml-2\" type=\"button\" data-clipboard-target=\"#citeACL\"><i class=\"far fa-clipboard\"><\/i><\/button> (CCF A top conference)<\/p>\n<p><i>Zichao Lin, Shuyan Guan, Wending Zhang, Huiyan Zhang, Yugang Li &amp; Huaping Zhang.<\/i>&nbsp;<a href=\"https:\/\/doi.org\/10.1007\/s10462-024-10896-y\">Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models<\/a>.&nbsp;<i>Artificial Intelligence Review<\/i>&nbsp;<b>57<\/b>, 243 (2024). https:\/\/doi.org\/10.1007\/s10462-024-10896-y (SCI Q1, \u4e2d\u79d1\u96622\u533a\uff0cImpactor Factor: 11.7)<\/p>\n<p>Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang*,STMAP: A novel semantic text matching model augmented with embedding perturbations,<br>Information Processing &amp; Management,Volume 61, Issue 1,2024,103576,ISSN 0306-4573,https:\/\/doi.org\/10.1016\/j.ipm.2023.103576.<br>(https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306457323003138\uff0cSCI \u4e2d\u79d1\u96621\u533a\uff0cImpactor Factor: 8.6\uff0c\u9876\u520a)<\/p>\n<p>Baohua Zhang, Jiahao Cai, Huaping Zhang*, Jianyun Shang,VisPhone: Chinese named entity recognition model enhanced by visual and phonetic features,<em><i>Information Processing &amp; Management<\/i><\/em>,Volume 60, Issue 3,2023,103314,ISSN 0306-4573,<a href=\"https:\/\/doi.org\/10.1016\/j.ipm.2023.103314\">https:\/\/doi.org\/10.1016\/j.ipm.2023.103314<\/a>.(<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306457323000511\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306457323000511<\/a>) (SCI \u4e2d\u79d1\u96621\u533a\uff0cImpactor Factor:8.6\uff0c\u9876\u520a)<\/p>\n<p>Baohua Zhang, Yongyi Huang, Wenyao Cui, Zhang Huaping, and Jianyun Shang. 2023. PsyAttention: Psychological Attention Model for Personality Detection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3398-3411, Singapore. Association for Computational Linguistics.<\/p>\n<article data-content=\"[{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;37Ms-1765280017769&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{&quot;version&quot;:1},&quot;nodes&quot;:[{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;L3Y1-1768821497678&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\/\/dblp.org\/pid\/409\/8575.html&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;ipZd-1768821497683&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Longyi&nbsp;Ye&quot;,&quot;marks&quot;:[]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;RmAp-1768821497685&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;,&nbsp;Huaping&nbsp;Zhang:&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#505b62&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:16}]},{&quot;text&quot;:&quot;LLMProto:&nbsp;A&nbsp;Hardware-Efficient&nbsp;Finetuning&nbsp;Model&nbsp;for&nbsp;Few-Shot&nbsp;Relation&nbsp;Extraction&nbsp;with&nbsp;Large&nbsp;Language&nbsp;Model.&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;bold&quot;},{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#666666&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:16}]},{&quot;text&quot;:&quot;&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#505b62&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:16}]}]},{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;D4zY-1768821497681&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\/\/dblp.org\/db\/conf\/icassp\/icassp2025.html#YeZ25&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;6Oy4-1768821497688&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;ICASSP&nbsp;2025&quot;,&quot;marks&quot;:[]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;NKIH-1768821497689&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;:&nbsp;1-5&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#505b62&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:16}]}]}],&quot;state&quot;:{}}]\">\n<div><a href=\"https:\/\/dblp.org\/pid\/409\/8575.html\">Longyi Ye<\/a>,&nbsp;Huaping Zhang: LLMProto: A Hardware-Efficient Finetuning Model for Few-Shot Relation Extraction with Large Language Model.&nbsp;<a href=\"https:\/\/dblp.org\/db\/conf\/icassp\/icassp2025.html#YeZ25\">ICASSP&nbsp;2025<\/a>:&nbsp;1-5<\/div>\n<\/article>\n<p>R. Yan, H. Zhang*, W. Silamu and A. Hamdulla, &#8220;Unsupervised word Segmentation Based on Word Influence,&#8221;&nbsp;<em>ICASSP 2023 &#8211; 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)<\/em>, Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109\/ICASSP49357.2023.10096718. (\u4fe1\u53f7\u5904\u7406\u56fd\u9645\u9876\u4f1a)<\/p>\n<p>Wenyao Cui, Jiahao Cai, Baohua Zhang, Yongyi Huang, Huaping Zhang*, &#8220;BRIDGING THE GAP: A SELF-LEARNING MODEL USING IMPLICIT KNOWLEDGE FOR CHINESE SPELLING CORRECTION&#8221;&nbsp;<em>ICASSP 2024 &#8211; 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)<\/em>, Seoul, Korea,&nbsp; 2024, pp. 1-5, doi:&nbsp; &nbsp;( \u4fe1\u53f7\u5904\u7406\u56fd\u9645\u9876\u4f1a)<\/p>\n<article data-content=\"[{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;hbgV-1766639447322&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;rGLo-1766639447320&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Geleta&nbsp;Negasa&nbsp;Binegde,&nbsp;Huaping&nbsp;Zhang,Exploring&nbsp;cultural&nbsp;commonsense&nbsp;in&nbsp;multilingual&nbsp;large&nbsp;language&nbsp;models:&nbsp;A&nbsp;survey,Information&nbsp;Systems,Volume&nbsp;138,2026,102649,ISSN&nbsp;0306-4379,&quot;,&quot;marks&quot;:[]}]},{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;2XVL-1765280017774&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\/\/doi.org\/10.1016\/j.is.2025.102649.&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;OqEZ-1765280017773&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;https:\/\/doi.org\/10.1016\/j.is.2025.102649.&quot;,&quot;marks&quot;:[]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;UWCP-1765280017776&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;\u3001&quot;,&quot;marks&quot;:[]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;37Ms-1765280017769&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{&quot;version&quot;:1},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;qyVh-1765280017770&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Brahimi,&nbsp;Nihad,&nbsp;Huaping&nbsp;Zhang,&nbsp;and&nbsp;Zahid&nbsp;Razzaq.&nbsp;2025.&nbsp;\\&quot;Quantum-Inspired&nbsp;Spatio-Temporal&nbsp;Inference&nbsp;Network&nbsp;for&nbsp;Sustainable&nbsp;Car-Sharing&nbsp;Demand&nbsp;Prediction\\&quot;&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]},{&quot;text&quot;:&quot;Sustainability&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;},{&quot;type&quot;:&quot;italic&quot;}]},{&quot;text&quot;:&quot;&nbsp;17,&nbsp;no.&nbsp;11:&nbsp;4987.&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]},{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;xX4v-1748522354425&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\/\/doi.org\/10.3390\/su17114987&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;3z0O-1748522354424&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;https:\/\/doi.org\/10.3390\/su17114987&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;}]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;5fIY-1748522354426&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;8Ea1-1748522352080&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;SeHe-1748522352078&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]}],&quot;state&quot;:{}}]\">\n<div>Geleta Negasa Binegde, Huaping Zhang,Exploring cultural commonsense in multilingual large language models: A survey,Information Systems,Volume 138,2026,102649,ISSN 0306-4379,<a href=\"https:\/\/doi.org\/10.1016\/j.is.2025.102649.\">https:\/\/doi.org\/10.1016\/j.is.2025.102649.<\/a><\/div>\n<div>&nbsp;<\/div>\n<\/article>\n<p>Brahimi, Nihad, Huaping Zhang, and Zahid Razzaq. 2025. &#8220;Quantum-Inspired Spatio-Temporal Inference Network for Sustainable Car-Sharing Demand Prediction&#8221;&nbsp;<em>Sustainability<\/em>&nbsp;17, no. 11: 4987. https:\/\/doi.org\/10.3390\/su17114987<\/p>\n<p>Brahimi, N.; Zhang, H.; Zaidi, S.D.A.; Dai, L. A Unified Spatio-Temporal Inference Network for Car-Sharing Serial Prediction.&nbsp;<em>Sensors<\/em>&nbsp;<b>2024<\/b>,&nbsp;<em>24<\/em>, 1266. <a href=\"https:\/\/doi.org\/10.3390\/s24041266\">https:\/\/doi.org\/10.3390\/s24041266<\/a>&nbsp; <span style=\"font-size: inherit;\"> ( SCI indexed, JCR Q2,\u4e2d\u79d1\u9662\u533a3\u533a Impactor Factor: 3.9)<\/span><\/p>\n<p>Zhang B, Zhang H*, Shang J and Cai J (2022) <a href=\"http:\/\/journal.frontiersin.org\/article\/10.3389\/fnbot.2022.897402\/full?&amp;utm_source=Email_to_authors_&amp;utm_medium=Email&amp;utm_content=T1_11.5e1_author&amp;utm_campaign=Email_publication&amp;field=&amp;journalName=Frontiers_in_Neurorobotics&amp;id=897402\">An Augmented Neural Network for Sentiment Analysis Using Grammar<\/a>.&nbsp;<i>Front. Neurorobot.<\/i> 16:897402. doi: 10.3389\/fnbot.2022.897402 <span style=\"font-size: inherit;\"> ( SCI indexed, JCR Q2,\u4e2d\u79d1\u9662\u533a3\u533a Impactor Factor: 3.493)<\/span><\/p>\n<p><span style=\"font-size: revert; color: initial;\">Li, L.H., Zhang, H.P., Li, C.J., et al.<\/span><a style=\"font-size: revert;\" href=\"https:\/\/direct.mit.edu\/dint\/article\/doi\/10.1162\/dint_a_00232\/117502\/Evaluation-on-ChatGPT-for-Chinese-Language\">: Evaluation on ChatGPT for Chinese Language Understanding<\/a><span style=\"font-size: revert; color: initial;\">. Data Intelligence&nbsp;<\/span>5(4) (2023).1-19 doi: https:\/\/doi.org\/10.1162\/dint_a_00232&nbsp; (SCI \u4e09\u533a)<\/p>\n<p>A. Khan, N. Boudjellal, Huaping Zhang*, A. Ahmad, M. 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Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-031-17120-8_52<\/span><\/p>\n<p>\u5f20\u534e\u5e73, \u674e\u6797\u7ff0, \u674e\u6625\u9526.&nbsp;ChatGPT\u4e2d\u6587\u6027\u80fd\u6d4b\u8bc4\u4e0e\u98ce\u9669\u5e94\u5bf9*[J]. \u6570\u636e\u5206\u6790\u4e0e\u77e5\u8bc6\u53d1\u73b0, 2023, 0(0): 1-10 <a href=\"https:\/\/mp.weixin.qq.com\/s\/_yIDKHwhSHflxyuDUcqr-g\">doi:10.11925\/infotech.2096-3467.2023.0214<\/a><\/p>\n<p>\u5f20\u534e\u5e73, \u674e\u6797\u7ff0, \u674e\u6625\u9526.&nbsp;ChatGPT\u4e2d\u6587\u7406\u89e3\u80fd\u529b\u6d4b\u8bc4\u4e0e\u98ce\u9669\u5e94\u5bf9\u7b56\u7565\u7814\u7a76*[J]. \u4e2d\u56fd\u4eba\u5de5\u667a\u80fd\u5b66\u4f1a\u901a\u8baf, 2023, 13(5): 46-52&nbsp;<\/p>\n<article data-content=\"[{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;DwtO-1744372426873&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;0sxP-1744372426874&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Brahimi,&nbsp;Nihad,&nbsp;Huaping&nbsp;Zhang,&nbsp;and&nbsp;Zahid&nbsp;Razzaq.&nbsp;2025.&nbsp;\\&quot;Quantum-Inspired&nbsp;Spatio-Temporal&nbsp;Inference&nbsp;Network&nbsp;for&nbsp;Sustainable&nbsp;Car-Sharing&nbsp;Demand&nbsp;Prediction\\&quot;&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]},{&quot;text&quot;:&quot;Sustainability&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;},{&quot;type&quot;:&quot;italic&quot;}]},{&quot;text&quot;:&quot;&nbsp;17,&nbsp;no.&nbsp;11:&nbsp;4987.&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]},{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;xX4v-1748522354425&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\/\/doi.org\/10.3390\/su17114987&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;3z0O-1748522354424&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;https:\/\/doi.org\/10.3390\/su17114987&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;}]}]}]},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;5fIY-1748522354426&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;8Ea1-1748522352080&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;SeHe-1748522352078&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Brahimi,&nbsp;N.,&nbsp;Zhang,&nbsp;H.,&nbsp;&amp;&nbsp;Razzaq,&nbsp;Z.&nbsp;(2025).&nbsp;Explainable&nbsp;Spatio-Temporal&nbsp;Inference&nbsp;Network&nbsp;for&nbsp;Car-Sharing&nbsp;Demand&nbsp;Prediction.&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]},{&quot;text&quot;:&quot;ISPRS&nbsp;International&nbsp;Journal&nbsp;of&nbsp;Geo-Information&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;},{&quot;type&quot;:&quot;italic&quot;}]},{&quot;text&quot;:&quot;,&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]},{&quot;text&quot;:&quot;14&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;},{&quot;type&quot;:&quot;italic&quot;}]},{&quot;text&quot;:&quot;(4),&nbsp;163.&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]},{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;7BHN-1744372428243&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\/\/doi.org\/10.3390\/ijgi14040163&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;OgTz-1744372428242&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;https:\/\/doi.org\/10.3390\/ijgi14040163&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;}]}]}]},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;wDpg-1744372428244&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;&quot;,&quot;marks&quot;:[]}]}],&quot;state&quot;:{}}]\">\n<div>Brahimi, Nihad, Huaping Zhang, and Zahid Razzaq. 2025. &#8220;Quantum-Inspired Spatio-Temporal Inference Network for Sustainable Car-Sharing Demand Prediction&#8221;&nbsp;Sustainability&nbsp;17, no. 11: 4987. <a href=\"https:\/\/doi.org\/10.3390\/su17114987\">https:\/\/doi.org\/10.3390\/su17114987<\/a><\/div>\n<div>Brahimi, N., Zhang, H., &amp; Razzaq, Z. (2025). Explainable Spatio-Temporal Inference Network for Car-Sharing Demand Prediction.&nbsp;ISPRS International Journal of Geo-Information,&nbsp;14(4), 163. <a href=\"https:\/\/doi.org\/10.3390\/ijgi14040163\">https:\/\/doi.org\/10.3390\/ijgi14040163<\/a><\/div>\n<\/article>\n<p><span style=\"font-size: inherit;\">Nihad Brahimi, Huaping Zhang*, Lin Dai, Jianzi Zhang, &#8220;<\/span><a href=\"https:\/\/www.hindawi.com\/journals\/complexity\/2022\/8843000\/\"><span class=\"adjust-article-svg-size\" style=\"font-size: inherit;\">Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning<\/span><\/a><span style=\"font-size: inherit;\">&#8220;,&nbsp;<\/span><i style=\"font-size: inherit;\">Complexity<\/i><span style=\"font-size: inherit;\">, vol. 2022, Article ID 8843000, 20 pages, 2022. https:\/\/doi.org\/10.1155\/2022\/8843000 ( SCI indexed, JCR Q1,\u4e2d\u79d1\u9662\u533a3\u533a Impactor Factor: 2.833)<\/span><\/p>\n<p>Asif Khan, <strong>Huaping Zhang*<\/strong>, Nada Boudjellal, Arshad Ahmad, Jianyun Shang, Lin Dai, Bashir Hayat, &#8220;<a href=\"https:\/\/www.hindawi.com\/journals\/complexity\/2021\/5565434\/\"><span class=\"adjust-article-svg-size\">Election Prediction on Twitter: A Systematic Mapping Study<\/span><\/a>&#8220;,&nbsp;<i>Complexity<\/i>, vol. 2021, Article ID 5565434, 27 pages, 2021. https:\/\/doi.org\/10.1155\/2021\/5565434( SCI indexed, JCR Q1,\u4e2d\u79d1\u9662\u533a3\u533a Impactor Factor: <span style=\"font-size: inherit;\">2.833<\/span>)<\/p>\n<p>Nada Boudjellal, <strong>Huaping Zhang*<\/strong>, Asif Khan, Arshad Ahmad, Rashid Naseem, Jianyun Shang, Lin Dai,&nbsp;<a href=\"https:\/\/www.hindawi.com\/journals\/complexity\/2021\/6633213\/\">&#8220;ABioNER: A BERT-Based Model for Arabic Biomedical Named-Entity Recognition&#8221;<\/a>,&nbsp;<i>Complexity<\/i>, vol. 2021, Article ID 6633213, 6 pages, 2021. https:\/\/doi.org\/10.1155\/2021\/6633213 ( SCI indexed, JCR Q1,\u4e2d\u79d1\u9662\u533a2\u533a Impactor Factor: 2.462)<\/p>\n<p>Nada Boudjellal, <strong>Huaping Zhang*<\/strong>, Asif Khan, Arshad Ahmad, Rashid Naseem, Lin Dai, &#8220;<a href=\"https:\/\/www.hindawi.com\/journals\/complexity\/2020\/8896659\/\">A Silver Standard Biomedical Corpus for Arabic Language<\/a>&#8220;,&nbsp;<i>Complexity<\/i>, vol. 2020, Article ID 8896659, 7 pages, 2020. https:\/\/doi.org\/10.1155\/2020\/8896659 ( SCI indexed, JCR Q1,\u4e2d\u79d1\u9662\u533a2\u533a Impactor Factor: <span style=\"font-size: inherit;\">2.833<\/span>)<\/p>\n<p>Asif Khan, <strong>Huaping Zhang*<\/strong>(Corresponding Author), Jianyun Shang, Nada Boudjellal, Arshad Ahmad, Asmat Ali, Lin Dai, &#8220;<a href=\"https:\/\/www.hindawi.com\/journals\/sp\/2020\/9353120\/\">Predicting Politician\u2019s Supporters\u2019 Network on Twitter Using Social Network Analysis and Semantic Analysis<\/a>&#8220;,&nbsp;<i>Scientific Programming<\/i>, vol. 2020, Article ID 9353120, 17 pages, 2020. https:\/\/doi.org\/10.1155\/2020\/9353120 \uff08SCI indexed, JCR Q2, Impactor 0.963\uff09<\/p>\n<p>Nada Boudjellal, <strong>Huaping Zhang*<\/strong>(Corresponding Author), Asif Khan, Arshad Ahmad, &#8220;<a href=\"https:\/\/www.hindawi.com\/journals\/sp\/2020\/8893749\/\">Biomedical Relation Extraction Using Distant Supervision<\/a>&#8220;,&nbsp;<i>Scientific Programming<\/i>, vol. 2020, Article ID 8893749, 9 pages, 2020. https:\/\/doi.org\/10.1155\/2020\/8893749\uff08SCI indexed, JCR Q2, Impactor 0.963\uff09<\/p>\n<p>A. Khan, H. Zhang, N. Boudjellal, B. Hayat, L. Dai, A. Ahmad, A. Al-Hamed, Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media, in: International Conference on Information Technology and Applications. 16th ICITA 2022, Springer, Cham, 2023, pp. 163\u2013173. doi: 10.1007\/978-981-19-9331-2_14. (EI\/ISI\/SCOPUS) <br>A. Khan, H. Zhang, N. Boudjellal, L. Dai, A. Ahmad, J. Shang, P. Haindl, A Comparative Study Between Rule-Based and Transformer-Based Election Prediction Approaches: 2020 US Presidential Election as a Use Case, in: Database Expert Syst. Appl. \u2013 33rd DEXA 2022, Springer, Cham, 2022: pp. 32\u201343. http:\/\/doi.org\/10.1007\/978-3-031-14343-4_4. (CCF\/EI\/ISI\/SCOPUS)<\/p>\n<p>\u5f20\u534e\u5e73,\u5f20\u5b9d\u534e,\u674e\u5fd7\u5f3a, \u6768\u8513\u829d,\u6768\u5b50\u7814\uff0c\u4e25\u82e5\u8c6a.\u57fa\u4e8e\u8bcd\u4e32\u7684\u5c0f\u8bed\u79cd\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u53ca\u8bed\u8a00\u5206\u6790\u6280\u672f[J]. \u4e2d\u56fd\u4eba\u5de5\u667a\u80fd\u5b66\u4f1a\u901a\u8baf, 2022, 3: 16-21.<\/p>\n<p>\u5f20\u5b9d\u534e, \u5f20\u534e\u5e73, \u5389\u94c1\u5e05, \u5546\u5efa\u4e91. \u57fa\u4e8e\u591a\u8f93\u5165\u6a21\u578b\u53ca\u53e5\u6cd5\u7ed3\u6784\u7684\u4e2d\u6587\u8bc4\u8bba\u60c5\u611f\u5206\u6790\u65b9\u6cd5[J]. \u5927\u6570\u636e, 2021, 7(6): 41-52.<\/p>\n<p>\u5f20\u534e\u5e73 \u5f20\u82af\u94ed \u5434\u6797\u82b3 \u674e\u660c\u8d6b \u5546\u5efa\u4e91;\u5c0f\u6837\u672c\u77e5\u8bc6\u56fe\u8c31\u7684\u6784\u5efa\u4e0e\u5e94\u7528;\u4eba\u5de5\u667a\u80fd;2020\u5e74\u7b2c2\u671f,p114-126<\/p>\n<p>Huaping Zhang1, Jun Miao2, Ziyu Liu1, Ian Logan Wesson1, Jianyun Shang1;NLPIR-Parser: Making Chinese and English Semantic Analysis Easier and Complete;15th International Conference on the Statistical Analysis of Textual Data;France\uff0c 2020.8&nbsp;<\/p>\n<p>\u5f20\u534e\u5e73\uff0c\u5546\u5efa\u4e91.<a href=\"http:\/\/www.nlpir.org\/wordpress\/wp-content\/uploads\/2019\/09\/\u5f20\u534e\u5e73\u3001\u5546\u5efa\u4e91_NLPIR-Parser\u5927\u6570\u636e\u8bed\u4e49\u667a\u80fd\u5206\u6790\u5e73\u53f0.pdf\">NLPIR-Parser\uff1a\u5927\u6570\u636e\u8bed\u4e49\u667a\u80fd\u5206\u6790\u5e73\u53f0<\/a>.\u8bed\u6599\u5e93\u8bed\u8a00\u5b66.2019\u5e74\u7b2c6\u5377\u7b2c1\u671f\uff0c87-104. 2019\u5e749\u6708<\/p>\n<p>\u5f20\u534e\u5e73\uff0c\u5546\u5efa\u4e91.\u9762\u5411\u793e\u4f1a\u5a92\u4f53\u7684\u5f00\u653e\u9886\u57df\u65b0\u8bcd\u53d1\u73b0.\u4e2d\u6587\u4fe1\u606f\u5b66\u62a5.\u7b2c31\u5377\u7b2c3\u671f\uff0c115-121. 2017\u5e745\u6708<br>\u5f20\u534e\u5e73\uff0c\u674e\u6052\u8bad\uff0c\u674e\u6e05\u654f. \u60c5\u611f\u8bcd\u53d1\u73b0\u4e0e\u6781\u6027\u6743\u91cd\u81ea\u52a8\u8ba1\u7b97\u7b97\u6cd5\u7814\u7a76.\u4e2d\u6587\u4fe1\u606f\u5b66\u62a5.\u7b2c31\u5377\u7b2c3\u671f\uff0c60-66. 2017\u5e745\u6708<\/p>\n<p>\u5f20\u534e\u5e73,\u5b59\u68a6\u59dd,\u5f20\u745e\u7426,\u674e\u857e.\u5fae\u535a\u535a\u4e3b\u7684\u7279\u5f81\u4e0e\u884c\u4e3a\u5927\u6570\u636e\u6316\u6398.\u4e2d\u56fd\u8ba1\u7b97\u673a\u5b66\u4f1a\u901a\u8baf.2014.6.p36-p43<\/p>\n<p>Huaping&nbsp;Zhang,Ruiqi&nbsp;Zhang,Yanping&nbsp;Zhao,Baojun&nbsp;Ma;Big&nbsp;Data&nbsp;Modeling&nbsp;and&nbsp;Analysis&nbsp;of&nbsp;Microblog&nbsp;Ecosystem;International&nbsp;Journal&nbsp;of&nbsp;Automation&nbsp;and&nbsp;Computing,2014.11(2)&nbsp;p119-127<\/p>\n<p>Baojun Ma,Huaping Zhao,Guoqing Chen,Yanping Zhao;Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective;Int&#8217;l Journal of Software Engineering and Knowledge Engineering,2013\uff08 SCI Indexed\uff09<\/p>\n<p>Hua-Ping ZHANG, Jian Sun, Bin WANG, Shuo BAI. Computation on Sentence Semantic Distance for Novelty Detection; Chinese Journal of Computer Science and Tech. vol.3, 2005\uff08 SCI Indexed\uff09<\/p>\n<p>\u5f20\u534e\u5e73,\u5218\u7fa4. \u57fa\u4e8e\u89d2\u8272\u6807\u6ce8\u7684\u4e2d\u56fd\u4eba\u540d\u81ea\u52a8\u8bc6\u522b\u7814\u7a76. \u8ba1\u7b97\u673a\u5b66\u62a5, vol.27, No.1, 2004, pp.85-91&nbsp;&nbsp; \u88ab\u5f15\u752831\u6b21<br>\u5218\u7fa4,\u5f20\u534e\u5e73,\u4fde\u9e3f\u9b41,\u7a0b\u5b66\u65d7. \u57fa\u4e8e\u5c42\u53e0\u9690\u9a6c\u6a21\u578b\u7684\u6c49\u8bed\u8bcd\u6cd5\u5206\u6790; \u8ba1\u7b97\u673a\u7814\u7a76\u4e0e\u53d1\u5c55, 41\u5377, No.8, 2004, pp.1421-pp.1429&nbsp; \u88ab\u5f15\u752843\u6b21<br>Hua-Ping ZHANG, Qun LIU, Hong-Kui YU, Xue-Qi CHENG, Shuo BAI. Chinese Name Entity Recognition Using Role Model.&nbsp; Special issue &#8220;Word Formation and Chinese Language processing&#8221; of the International Journal of Computational Linguistics and Chinese Language Processing, vol.8, No.2, 2003, pp. 29-602<br>\u5f20\u534e\u5e73,\u5218\u7fa4.\u57fa\u4e8eN-\u6700\u77ed\u8def\u5f84\u7684\u4e2d\u6587\u8bcd\u8bed\u7c97\u5206\u6a21\u578b. \u4e2d\u6587\u4fe1\u606f\u5b66\u62a5. 2002.9, Vol.16(5):pp.1-pp.7;&nbsp; \u88ab\u5f15\u752834\u6b21<\/p>\n<h2><strong>\u6559\u5b66\u60c5\u51b5\uff1a<\/strong><\/h2>\n<p>\u7814\u7a76\u751f\u6559\u5b66\uff1a<a href=\"http:\/\/www.nlpir.org\/wordpress\/2018\/04\/01\/%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E4%B8%8E%E5%BA%94%E7%94%A8%E7%A1%95%E5%A3%AB%E7%8F%AD%E8%AF%BE%E7%A8%8B\/\">\u5927\u6570\u636e\u5206\u6790\u4e0e\u5e94\u7528<\/a><\/p>\n<p>\u672c\u79d1\u6559\u5b66\uff1a\u5927\u6570\u636e\u6280\u672f&nbsp; \u6c47\u7f16\u8bed\u8a00\u4e0e\u63a5\u53e3\u3001C\u8bed\u4e49\u7a0b\u5e8f\u8bbe\u8ba1<\/p>\n<p>\u6307\u5bfc\u5b66\u672f\u6bd4\u8d5b\u83b7\u5956\u60c5\u51b5<\/p>\n<p>2023\u5e74 \u4e2d\u56fd\u4eba\u5de5\u667a\u80fd\u5b66\u4f1a \u591a\u8bed\u79cd\u56fe\u50cf\u63cf\u8ff0\u751f\u6210\u6d4b\u8bc4\u6bd4\u8d5b\uff0c\u7ef4\u543e\u5c14\u8bed\u5168\u56fd\u7b2c\u4e00\u540d\uff0c\u85cf\u8bed\u7b2c\u4e8c\u540d\uff0c\u8499\u53e4\u8bed\u7b2c\u4e8c\u540d\u3002<\/p>\n<p>2022\u5e74\u201c\u4e0a\u6d77-\u9999\u6e2f\u8de8\u5b66\u79d1\u793e\u4ea4\u5a92\u4f53\u5206\u6790\u8054\u5408\u8bc4\u6d4b\u201d\uff1a\u201c\u591a\u8bed\u8a00\u8bed\u5883\u4e0b\u7684\u60c5\u611f\u5206\u6790\u8d5b\u9053\u201d\u51b3\u8d5b\u7b2c\u4e00\u540d\u3001\u201c\u9762\u5411\u8bdd\u9898\u7684\u7fa4\u4f53\u60c5\u7eea\u8bc6\u522b\u6bd4\u8d5b\u201d\u51b3\u8d5b\u7b2c\u4e09\u540d\uff1b\u738b\u5f66\u6d69\uff0c\u8521\u4f73\u8c6a\uff0c\u6307\u5bfc\u8001\u5e08\uff1a\u5f20\u534e\u5e73<\/p>\n<p>\u4e2d\u56fd\u4e2d\u6587\u4fe1\u606f\u5b66\u4f1a \u4e34\u5e8a\u4e8b\u4ef6\u62bd\u53d6\uff0c\u7b2c\u4e00\u540d\uff0c\u5eb7\u94e0\u3001\u5b8b\u82e5\u8bed\u3001\u675c\u4f26\uff1b\u4e2d\u56fd\u5065\u5eb7\u4fe1\u606f\u5904\u7406\u4f1a\u8bae(CHIP2021)\uff1b\u6307\u5bfc\u8001\u5e08\uff1a\u5f20\u534e\u5e73\u3001\u90ed\u5b87\u822a<\/p>\n<p>2022\u5e74\u4e2d\u56fd\u5de5\u4e1aAPP\u7ade\u8d5b\u4e09\u7b49\u5956 \u6bd4\u7279\u79d8\u4e66\uff0c\u6c64\u6cfd\u9633\uff0c\u674e\u80b2\u9716\u7b49\uff0c\u6307\u5bfc\u8001\u5e08\uff1a\u5f20\u534e\u5e73<\/p>\n<p>2021\u5e74\u795e\u5dde\u4fe1\u606f\u6781\u5ba2\u5927\u8d5b\uff0cNLP\u7b97\u6cd5\u5e94\u7528\u8d5b\u9053\u7b2c\u4e8c\u540d\uff1b\u5eb7\u94e0\uff0c\u6768\u5b50\u7814\uff1b\u6307\u5bfc\u8001\u5e08\uff1a \u5f20\u534e\u5e73\uff0c\u674e\u7389\u5c97<br>2021\u5e74\uff0c\u7b2c\u4e09\u5c4a\u4e2d\u56fdAI+\u521b\u65b0\u521b\u4e1a\u5927\u8d5b\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6280\u672f\u521b\u65b0\u5927\u8d5b\u4e2d\u6587\u6587\u672c\u7ea0\u9519\u6bd4\u8d5b\u4e09\u7b49\u5956\uff08\u5168\u56fd\u6392\u540d\u7b2c\u56db\uff09\uff1b\u8521\u4f73\u8c6a\u3001\u8c0c\u7acb\u51e4\u3001\u96f7\u6c9b\u53ef\uff1b\u6307\u5bfc\u8001\u5e08\uff1a\u5f20\u534e\u5e73\uff0c\u5546\u5efa\u4e91<\/p>\n<h2><strong>\u4e3b\u6301\u7684\u79d1\u7814\u8bfe\u9898\uff1a<\/strong><\/h2>\n<p>\u8bfe\u9898\u540d\u79f0&nbsp;\u6765\u6e90&nbsp;\u8d77\u59cb\u65e5\u671f&nbsp;\u5b8c\u6210\u4eba\u987a\u5e8f<\/p>\n<p>\u56fd\u5bb6\u7f51\u7edc\u7a7a\u95f4\u5b89\u5168\u91cd\u5927\u4e13\u9879\uff0c\u4e2d\u5173\u6751\u5b9e\u9a8c\u5ba4\uff0c\u751f\u6210\u5f0fAI\u5185\u5bb9\u5b89\u5168\u68c0\u6d4b\u6280\u672f\u7814\u7a76\uff082025ZD1502903 \uff09,2025-12\u81f32028-12,1750\u4e07\u5143,\u5728\u7814,\u4e3b\u6301<\/p>\n<p>\u57fa\u4e8e\u4ea4\u6613\u884c\u4e3a\u7279\u5f81\u7684\u503a\u5238\u6295\u8d44\u4eba\u957f\u77ed\u671f\u5168\u606f\u753b\u50cf\uff1b <span class=\"fontstyle0\">\u56fd\u5bb6\u91cd\u70b9\u7814\u53d1\u8ba1\u5212\u8bfe\u9898\uff082024YFC3308101\uff09<\/span>; 2025.1-2028.1;350\u4e07\uff1b\u5728\u7814 \u5f20\u534e\u5e73&nbsp;<\/p>\n<p><span class=\"fontstyle0\">\u57fa\u4e8e\u591a\u8bed\u77e5\u8bc6\u4e0e\u5c0f\u6837\u672c\u5b66\u4e60\u7684\u5c0f\u8bed\u79cd\u673a\u5668\u7ffb\u8bd1\u4e0e\u5185\u5bb9\u751f\u6210\u6280\u672f<\/span>\uff1b <span class=\"fontstyle0\">\u57fa\u7840\u52a0\u5f3a\u8ba1\u5212\u6280\u672f\u9886\u57df\u57fa\u91d1\uff082021-JCJQ-JJ-0059\uff09<\/span>; 2021.10-2024.10;200\u4e07\uff1b\u5728\u7814 \u5f20\u534e\u5e73<\/p>\n<p>\u914d\u7f6e\u5b89\u5168\uff1b\u521b\u65b0\u7279\u533a\u9879\u76ee; 2021.11-2023.11;300\u4e07\uff1b\u5728\u7814 \u5f20\u534e\u5e73<\/p>\n<p>\u57fa\u4e8e\u8bed\u8a00\u9884\u8bad\u7ec3\u6a21\u578b\u4e0e\u9886\u57df\u77e5\u8bc6\u7684\u4e2d\u6587\u6587\u672c\u878d\u5408\u6821\u5bf9\u7b97\u6cd5\u7814\u7a76\uff1b\u5317\u4eac\u5e02\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u9762\u4e0a\u9879\u76ee(4212026); 2021.1-2024.12;20\u4e07\uff1b\u5728\u7814 \u5f20\u534e\u5e73<\/p>\n<p>\u6587\u672c\u751f\u6210\u9884\u7814\u8bfe\u9898\uff1bZF\u6f14\u793a\u9a8c\u8bc1; 2020.7-2021.12; 897\u4e07\uff1b\u5728\u7814 \u5f20\u534e\u5e73<\/p>\n<div>\n<div>\n<p><span style=\"font-size: inherit;\">\u8bed\u4e49\u4e3b\u9898\u4e0e\u793e\u4ea4\u5173\u7cfb\u878d\u5408\u7684\u7279\u5b9a\u7fa4\u4f53\u53d1\u73b0\u5173\u952e\u6280\u672f\u7814\u7a76\uff1b\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u9762\u4e0a\u9879\u76ee(61772075); 2018.1-2021.12;61\u4e07\uff1b\u5728\u7814 \u5f20\u534e\u5e73<\/span><\/p>\n<\/div>\n<div>\u9762\u5411\u4e92\u8054\u7f51\u4fe1\u606f\u7684\u53f8\u6cd5\u8206\u60c5\u76d1\u6d4b\u4e0e\u5206\u7ea7\u9884\u8b66\u6280\u672f\u7814\u7a76\u53ca\u7cfb\u7edf\u7814\u53d1 2018YFC0832304 \u201c\u516c\u5171\u5b89\u5168\u98ce\u9669\u9632\u63a7\u4e0e\u5e94\u6025\u6280\u672f\u88c5\u5907\u201d\u91cd\u70b9\u4e13\u9879\uff08\u53f8\u6cd5\u4e13\u9898\u4efb\u52a1\uff09 121<\/div>\n<div>&nbsp;<\/div>\n<\/div>\n<div>\n<div>\u65e5\u5fd7\u5206\u6790\u4e0e\u7528\u6237\u753b\u50cf* \u56fd\u5bb6242\u4fe1\u606f\u5b89\u5168\u8ba1\u5212 70 2016.6-2017.5 \u8bfe\u9898\u8d1f\u8d23\u4eba 2016A83<\/div>\n<p>\u4e2d\u56fd\u4eba\u6c11\u94f6\u884c\u5f81\u4fe1\u4e2d\u5fc3\u4e92\u8054\u7f51\u516c\u5171\u4fe1\u606f\u81ea\u52a8\u6293\u53d6\u8bfe\u9898\u7814\u7a76\u670d\u52a1\u4e8c\u6b21\u91c7\u8d2d\u9879\u76ee 2015.5-10 \u8d1f\u8d23\u4eba<br>\u6587\u672c\u6316\u6398\u5173\u952e\u6280\u672f* \u56fd\u5bb6242\u4fe1\u606f\u5b89\u5168\u8ba1\u5212 80 2014.11-2015.11 \u8bfe\u9898\u8d1f\u8d23\u4eba 2014A10 \u793e\u4ea4\u7f51\u7edc\u5206\u6790\u53ca\u4fe1\u606f\u4f20\u64ad\u7406\u8bba\u5728\u8206\u60c5\u9884\u8b66\u65b9\u9762\u7684\u793a\u8303\u9a8c\u8bc1&nbsp;\u56fd\u5bb6973\u91cd\u70b9\u57fa\u7840\u7814\u7a76\u53d1\u5c55\u8ba1\u5212&nbsp;96&nbsp;2013.1-2017.12&nbsp;\u5b50\u8bfe\u9898\u8d1f\u8d23\u4eba\uff1a\u5f20\u534e\u5e73&nbsp;2013CB329606<br>\u57fa\u4e8e\u4e3b\u4f53\u4e2a\u6027\u5316\u7684\u5fae\u535a\u60c5\u611f\u5206\u6790\u5173\u952e\u6280\u672f\u7814\u7a76&nbsp;\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u9762\u4e0a\u9879\u76ee 84\u4e07&nbsp;2013.1-2016.12 \u57fa\u91d1\u53f7\uff1a61272362 \u8bfe\u9898\u8d1f\u8d23\u4eba\uff1a\u5f20\u534e\u5e73<br>\u65b0\u7586\u7ef4\u6587\u8bed\u8a00\u7f51\u7edc\u8206\u60c5\u76d1\u6d4b\u9884\u8b66\u7cfb\u7edf\u5f00\u53d1\u4e0e\u5e94\u7528 \u65b0\u7586\u81ea\u6cbb\u533a\u79d1\u6280\u652f\u6491\u8ba1\u5212\u8bfe\u9898 2012.1-2013.12 2\u8054\u5408\u7533\u8bf7\u5355\u4f4d\u8d1f\u8d23\u4eba<br>\u4e91\u8ba1\u7b97\u5b89\u5168\u9690\u60a3\u5206\u6790\u4e0e\u6218\u7565\u5bf9\u7b56\u7814\u7a76&nbsp;\u4e2d\u56fd\u4fe1\u606f\u5b89\u5168\u8bc4\u6d4b\u4e2d\u5fc3&nbsp;2010.11-2011.11&nbsp;1\u4e3b\u6301<br>\u79d1\u6280\u9879\u76ee\u5e93\u6784\u5efa\u4e0e\u6316\u6398\u7cfb\u7edf&nbsp;\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u4fe1\u606f\u7814\u7a76\u6240&nbsp;2010.11-2011.11&nbsp;1\u4e3b\u6301<br>\u57fa\u4e8e\u65f6\u7a7a\u5206\u6790\u7684\u6c14\u8c61\u516c\u62a5\u81ea\u52a8\u751f\u6210&nbsp;\u56fd\u5bb6\u6c14\u8c61\u5c40&nbsp;2010.9-2011.9&nbsp;1\u4e3b\u6301<br>\u4e2d\u56fd\u90ae\u653f\u5730\u5740\u641c\u7d22\u5f15\u64ce\u7cfb\u7edf&nbsp;\u4e2d\u56fd\u90ae\u653f\u96c6\u56e2&nbsp;2011.1-2011.3&nbsp;1\u4e3b\u6301<br>\u7f51\u7edc\u8bdd\u9898\u7684\u53d1\u73b0\u3001\u4f20\u64ad\u53ca\u52a8\u6001\u6f14\u5316\u7279\u5f81\u5206\u6790\u6280\u672f&nbsp;\u56fd\u5bb6863\u8ba1\u5212&nbsp;2007.9-2009.7&nbsp;1\u4e3b\u6301<br>\u57fa\u4e8e\u6d88\u606f\u7684\u70ed\u70b9\u53d1\u73b0\u4e0e\u4fe1\u606f\u8ffd\u8e2a*&nbsp;\u56fd\u5bb6242\u4fe1\u606f\u5b89\u5168\u8ba1\u5212&nbsp;2005.11-2006.11&nbsp;1\u4e3b\u6301<br>\u8f85\u52a9\u5206\u6790\u7cfb\u7edf*&nbsp;\u56fd\u5bb6242\u4fe1\u606f\u5b89\u5168\u8ba1\u5212&nbsp;2007.10-2008.4&nbsp;1\u4e3b\u6301<br>\u9762\u5411\u70ed\u70b9\u8bdd\u9898\u53d1\u73b0\u7cfb\u7edf*&nbsp;\u5de5\u4fe1\u90e8&nbsp;2008.12-2009.5&nbsp;1\u4e3b\u6301<br>**\u7684\u8206\u60c5**\u7814\u7a76*&nbsp;\u56fd\u5bb6242\u4fe1\u606f\u5b89\u5168\u8ba1\u5212&nbsp;2008.11-2009.10&nbsp;2\u526f\u7ec4\u957f<br>\u865a\u62df\u8ba1\u7b97\u73af\u5883\u4e0b\u7684\u7f51\u7edc\u8206\u60c5\u81ea\u9002\u5e94\u7cfb\u7edf\u7814\u7a76*&nbsp;\u56fd\u5bb6242\u4fe1\u606f\u5b89\u5168\u8ba1\u5212&nbsp;2008.11-2009.11&nbsp;2\u526f\u7ec4\u957f<br>\u9762\u5411\u4e92\u8054\u7f51\u7684\u53d1\u73b0\u7cfb\u7edf*&nbsp;\u5de5\u4fe1\u90e8&nbsp;2008.6-2009.5&nbsp;1\u4e3b\u6301<br>\u4e2d\u56fd\u8bc1\u76d1\u4f1a\u7f51\u7edc\u4fe1\u606f\u76d1\u63a7\u7cfb\u7edf&nbsp;\u4e2d\u56fd\u8bc1\u76d1\u4f1a&nbsp;2008.11-2009.6&nbsp;1\u4e3b\u6301<br>\u4e91\u8ba1\u7b97\u5b89\u5168\u6218\u7565\u7814\u7a76&nbsp;\u5b89\u5168\u90e8&nbsp;2010.11-2011.11&nbsp;1\u4e3b\u6301<br>\u793e\u4f1a\u8206\u60c5\u5206\u6790\u673a\u5236\u7814\u7a76&nbsp;2006\u6240\u77e5\u8bc6\u521b\u65b0\u57fa\u91d1&nbsp;2006.6-2007.6&nbsp;1\u4e3b\u6301<br>\u9762\u5411\u8bc1\u5238\u5e02\u573a\u7684\u8206\u60c5\u52a8\u6001\u5206\u6790&nbsp;\u4f18\u79c0\u535a\u58eb\u5b66\u4f4d\u8bba\u6587\u3001\u9662\u957f\u5956\u83b7\u5f97\u8005\u79d1\u7814\u542f\u52a8\u4e13\u9879\u8d44\u91d1&nbsp;2006.07\uff0d2007.6&nbsp;1\u4e3b\u6301<br>\u4e0a\u5e02\u516c\u53f8\u5546\u60c5\u6316\u6398\u7cfb\u7edf\u5f00\u53d1\u5e94\u7528&nbsp;2007\u8ba1\u7b97\u6240\u6240\u77e5\u8bc6\u521b\u65b0\u57fa\u91d1\u767e\u661f\u8ba1\u5212&nbsp;2007.11-2008.10&nbsp;1\u4e3b\u6301<br>\u57fa\u4e8e\u5927\u89c4\u6a21\u8ba1\u7b97\u5e73\u53f0\u7684\u4e92\u8054\u7f51\u6587\u672c\u4fe1\u606f\u641c\u7d22\u5f15\u64ce\u7cfb\u7edf&nbsp;\u4e2d\u56fd\u79fb\u52a8\u7814\u7a76\u9662&nbsp;2008.10-2009.6&nbsp;1\u4e3b\u6301<\/p>\n<pre id=\"activity-name\" class=\"rich_media_title \">&nbsp;<\/pre>\n<h2><strong>\u6307\u5bfc\u5b66\u751f\u60c5\u51b5\uff1a<\/strong><\/h2>\n<p>\u5b66\u751f\u59d3\u540d&nbsp;\u8eab\u4efd&nbsp;\u5e74\u4efd&nbsp;\u6bd5\u4e1a\u53bb\u5411<\/p>\n<p>\u5d14\u6587\u8000 \u5317\u4eac\u7406\u5de5\u5927\u5b66 2022 \u7855\u535a\u8fde\u8bfb\uff0c<\/p>\n<p>\u9ec4\u548f\u4eea \u5317\u4eac\u7406\u5de5\u5927\u5b66 2022 \u7855\u535a\u8fde\u8bfb\uff0c<\/p>\n<p>\u5f20\u7b11\u8bed \u5317\u4eac\u7406\u5de5\u5927\u5b66 2022 \u7855\u535a\u8fde\u8bfb\uff0c<\/p>\n<p>\u674e\u6797\u7ff0 \u5317\u4eac\u7406\u5de5\u5927\u5b66 2022 \u7855\u58eb\u5728\u8bfb\uff0c<\/p>\n<p>\u674e\u6797\u7ff0 \u5317\u4eac\u7406\u5de5\u5927\u5b66 2022 \u7855\u58eb\u5728\u8bfb\uff0c<\/p>\n<p>Asif Khan \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u535a\u8fde\u8bfb 2018&nbsp;\u5728\u8bfb(\u6765\u81ea\u5df4\u57fa\u65af\u5766)\uff0c\u7855\u58eb\u6bd5\u4e1a\u8bba\u6587\uff1aPolitician Supporter Social Network Analysis and Prediction on Twitter<\/p>\n<p>\u5f20\u5b9d\u534e \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2018 \u7855\u535a\u58eb\u5728\u8bfb\uff0c\u7855\u58eb\u6bd5\u4e1a\u9898\u76ee\uff1a\u57fa\u4e8e\u5c42\u6b21\u7ed3\u6784\u7684\u60c5\u611f\u8bed\u4e49\u5355\u5143\u8868\u793a\u53ca\u5206\u6790\u65b9\u6cd5<\/p>\n<p>\u8521\u4f73\u8c6a \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2020<\/p>\n<p>Nihad \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u535a\u58eb 2018 \u5728\u8bfb(\u6765\u81ea\u963f\u5c14\u53ca\u5229\u4e9a)<\/p>\n<p>IIham Nugraha \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u535a\u58eb 2018 \u5728\u8bfb(\u6765\u81ea\u5370\u5c3c)<\/p>\n<p>Nada Boudjellal \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u535a\u58eb 2017 \u5165\u5b66\uff0c2022\u5e746\u6708\u6bd5\u4e1a\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1aRESEARCH ON BIOMEDICAL KNOWLEDGE EXTRACTION FOR ARABIC LANGUAGE\uff1b\u963f\u62c9\u4f2f\u8bed\u751f\u7269\u533b\u5b66\u77e5\u8bc6\u62bd\u53d6\u7814\u7a76\uff0c\u8054\u5408\u6234\u6797\u8001\u5e08\u6307\u5bfc(\u6765\u81ea\u963f\u5c14\u53ca\u5229\u4e9a)<\/p>\n<p>\u674e\u660c\u8d6b \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2019\uff0c2022\u5e746\u6708\u6bd5\u4e1a\u5165\u804c\u4e2d\u56fd\u7535\u4fe1\u5929\u7ffc\u4e91\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u56fe\u7ed3\u6784\u4e0e\u8bed\u4e49\u7684\u8de8\u8bed\u8a00\u5b9e\u4f53\u5bf9\u9f50\u4f18\u5316\u7b97\u6cd5\u7814\u7a76<\/p>\n<p>\u5f20\u82af\u94ed \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2019\uff0c2022\u5e746\u6708\u6bd5\u4e1a\u5165\u804c \u77e5\u4e4e\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u5c0f\u6837\u672c\u6761\u4ef6\u4e0b\u77e5\u8bc6\u62bd\u53d6\u7b97\u6cd5\u7814\u7a76\uff0cKnowledge Extraction Algorithm Research in Few-shot Scenario<\/p>\n<p>\u5f20\u96bd\u9a70 \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2020\uff0c2022\u5e746\u6708\u6bd5\u4e1a\u5165\u804c\u963f\u91cc\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u591a\u53e5\u538b\u7f29\u7684\u79d1\u6280\u60c5\u62a5\u81ea\u52a8\u751f\u6210\u7b97\u6cd5\u7814\u7a76\u4e0e\u5e94\u7528\uff0c\u8054\u5408\u5546\u5efa\u4e91\u8001\u5e08\u6307\u5bfc<\/p>\n<p>\u675c\u535a\u8f69 \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2020\uff0c2022\u5e746\u6708\u6bd5\u4e1a\u5165\u804c\u4e2d\u56fd\u94f6\u884c\u4fe1\u606f\u4e2d\u5fc3\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u9762\u5411\u793e\u4f1a\u6cbb\u7406\u7684\u6587\u672c\u6807\u6ce8\u53ca\u5206\u7c7b\u6280\u672f\u7814\u7a76\u4e0e\u5e94\u7528\uff0c\u8054\u5408\u5546\u5efa\u4e91\u8001\u5e08\u6307\u5bfc<\/p>\n<p>\u5b59\u5a67\u5a67 \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2020\uff0c2022\u5e746\u6708\u6bd5\u4e1a\u5165\u804c\u7f8e\u56e2\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u591a\u6a21\u6001\u7684\u4eba\u7269\u6d3b\u52a8\u8ddf\u8e2a\u5173\u952e\u6280\u672f\u7814\u7a76\uff0c\u8054\u5408\u5546\u5efa\u4e91\u8001\u5e08\u6307\u5bfc<\/p>\n<p>\u5f20\u5b50\u70c1\uff0c\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2020\uff0c2022\u5e746\u6708\u6bd5\u4e1a\u5165\u804c\u4e2d\u56fd\u94f6\u884c\u4fe1\u606f\u4e2d\u5fc3\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u5883\u5916\u65b0\u95fb\u7684\u91d1\u878d\u666f\u6c14\u6307\u6570\u6a21\u578b\u6784\u5efa\u4e0e\u5e94\u7528\uff0c\u8054\u5408\u5546\u5efa\u4e91\u8001\u5e08\u6307\u5bfc<\/p>\n<p>\u59dc\u5e86\u9e3f \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2018 2021\u5e74\u6bd5\u4e1a\u5165\u804c\u9e4f\u626c\u57fa\u91d1\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u6f5c\u5728\u8bed\u4e49\u6316\u6398\u7684\u6587\u672c\u5206\u7c7b\u6280\u672f\u7684\u7814\u7a76<br>\u5218\u5b50\u5b87 \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2018 2021\u5e74\u6bd5\u4e1a\u5feb\u624b\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u77e5\u8bc6\u56fe\u8c31\u4e0e\u8bed\u4e49\u7279\u5f81\u7ed3\u5408\u7684\u4e2d\u6587\u8bed\u4e49\u6d88\u6b67\u5173\u952e\u6280\u672f\u7814\u7a76<br>\u738b\u521a \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2018 2021\u5e74\u6bd5\u4e1a\u5165\u804c\u641c\u72d7\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u81ea\u7136\u573a\u666f\u6587\u5b57\u8bc6\u522b\u6a21\u578b\u7684\u7814\u7a76<\/p>\n<p>Ian Logan Wesson, \u7855\u58eb\uff0c2018\u7ea7\uff0c2020\u5e74\u6bd5\u4e1a\uff0c\u8bba\u6587\uff08A Novel Multimodal Model for Event Detection in Videos\uff09,\u6765\u81ea\u7f8e\u56fd\u4f5b\u7f57\u91cc\u8fbe\u5dde<br>G. Sheryta Yvette, \u7855\u58eb\uff0c2018\u7ea7\uff0c2020\u5e74\u6bd5\u4e1a\u56de\u56fd\uff0c\u8bba\u6587\uff08Ewe Language Processing using Transfer Learning\uff09,\u6765\u81ea\u591a\u54e5<br>\u4e07\u91d1\u6676 \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2017\uff0c2020\u5e74\u6bd5\u4e1a\u5165\u804c\u822a\u5929\u8f6f\u4ef6\uff0c\u6bd5\u4e1a\u8bba\u6587\uff08\u57fa\u4e8e\u9886\u57df\u77e5\u8bc6\u7684\u6587\u672c\u6821\u5bf9\u5173\u952e\u6280\u672f\u7814\u7a76\uff09<\/p>\n<p>\u6768\u8000\u98de \u5317\u4eac\u4fe1\u606f\u79d1\u6280\u5927\u5b66\uff0c\u5317\u4eac\u7406\u5de5\u5927\u5b66\u5ba2\u5ea7\u7855\u58eb 2017\uff0c2020\u5e74\u6bd5\u4e1a\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u795e\u7ecf\u673a\u5668\u7ffb\u8bd1\u7684\u8bcd\u7d20\u7279\u5f81\u878d\u5408<\/p>\n<p>Iram Ishtiaq \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2016\uff0c2018\u5e74\u6bd5\u4e1a\u5165\u804c\u4eba\u5927\u9644\u4e2d\uff08\u5df4\u57fa\u65af\u5766\uff09\uff0c\u6bd5\u4e1a\u8bba\u6587: <strong>Named Entity Recognition in Personality Profiling<\/strong><\/p>\n<p>Ahmad Mafazi Damanhuri\uff1a\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2016\uff0c2018\u5e74 \u6bd5\u4e1a\u56de\u56fd\u4efb\u6559(\u5370\u5c3c), \u6bd5\u4e1a\u8bba\u6587: PEOPLE PROFILING AND MODELING REPUTATION COMPUTATION BASED ON SENTIMENT ANALYSIS<\/p>\n<p>\u9ad8\u8398&nbsp; &nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2016 \uff0c2019\u5e74\u6bd5\u4e1a\u5317\u5927\u8bfb\u535a\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u795e\u7ecf\u7f51\u7edc\u7684\u6587\u672c\u8868\u793a\u4e0e\u5e94\u7528\u7814\u7a76<br>\u5218\u658c&nbsp;&nbsp; \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2016 \uff0c2019\u5e74\u6bd5\u4e1a\u5165\u804c\u4e2d\u56fd\u8054\u901a\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8eMask-RCNN\u7684\u56fe\u50cf\u4e2d\u6587\u63cf\u8ff0\u751f\u6210\u5668<br>\u5f20\u73ba&nbsp; &nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2016 \u8bfb\u535a\uff1b\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u5e8f\u5217\u5230\u5e8f\u5217\u7684\u751f\u6210\u5f0f\u6587\u672c\u6458\u8981\u7814\u7a76<br>\u5f90\u7a0b\u7a0b&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2015\uff0c2018\u5e74\u6bd5\u4e1a\u5165\u804c \u767e\u5ea6\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u793e\u4ea4\u5173\u7cfb\u548c\u4e3b\u9898\u7279\u5f81\u878d\u5408\u7684\u793e\u533a\u53d1\u73b0<br>\u5434\u677e\u6cfd&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2015\uff0c2018\u5e74\u6bd5\u4e1a\u5165\u804c \u767e\u5ea6\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u793e\u4ea4\u7f51\u7edc\u8bdd\u9898\u53d1\u73b0\u5173\u952e\u6280\u672f\u7814\u7a76<br>\u5f20\u4e9a\u7537&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2015\uff0c2017\u5e74\u6bd5\u4e1a\u5165\u804c \u767e\u5ea6\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u516c\u5b89\u975e\u7ed3\u6784\u5316\u5927\u6570\u636e\u6316\u6398\u5173\u952e\u6280\u672f\u4e0e\u5e94\u7528<br>\u5362\u5175\u5175&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2015\uff0c2018\u5e74\u6bd5\u4e1a\u5165\u804c \u90e8\u961f\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8eHadoop\u5e73\u53f0\u7684VPN\u65e5\u5fd7\u7528\u6237\u6316\u6398\u5206\u6790<br>\u4e8e &nbsp;\u654f&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2014\uff0c2016\u5e7412\u6708\u6bd5\u4e1a \u798f\u5efa\u4e09\u660e\u5b66\u9662\u4efb\u6559\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u7a7a\u95f4\u5206\u6790\u7684\u6c14\u8c61\u9884\u62a5\u6587\u672c\u5b9e\u65f6\u751f\u6210\u5173\u952e\u6280\u672f\u7814\u7a76<\/p>\n<p>\u4e54 &nbsp;\u6768&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2014 \uff0c2016\u5e7412\u6708\u6bd5\u4e1a \u4e2d\u56fd\u79fb\u52a8\u7814\u7a76\u9662\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u9762\u5411\u793e\u4ea4\u7f51\u7edc\u7684\u8c23\u8a00\u68c0\u6d4b\u4e0e\u6c34\u519b\u53d1\u73b0<br>\u5f20 &nbsp;\u96e8&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2014\uff0c2016\u5e7412\u6708\u6bd5\u4e1a &nbsp; \u767e\u5ea6\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u9762\u5411\u793e\u4f1a\u5a92\u4f53\u7684\u7279\u5b9a\u4e8b\u4ef6\u5f71\u54cd\u529b\u8ba1\u7b97\u5173\u952e\u6280\u672f\u7814\u7a76<br>\u8d75\u8fde\u4f1f&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2013\uff0c2015\u5e7412\u6708\u6bd5\u4e1a &nbsp;\u4e2d\u56fd\u519c\u4e1a\u94f6\u884c\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u9762\u5411\u65b0\u5a92\u4f53\u7684\u65b0\u95fb\u7f29\u5199\u5173\u952e\u6280\u672f\u7814\u7a76<\/p>\n<p>\u5eb7\u8096\u94b0 \u9996\u90fd\u5e08\u8303\u5927\u5b66\uff0c\u5317\u4eac\u7406\u5de5\u5927\u5b66\u5ba2\u5ea7\u7855\u58eb 2013\uff0c2015\u5e7412\u6708\u6bd5\u4e1a\u5165\u804c\u4e2d\u79d1\u9662\u4fe1\u5de5\u6240\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u9762\u5411\u793e\u4ea4\u7f51\u7edc\u7684\u52a8\u6001\u4fe1\u606f\u81ea\u52a8\u53d1\u73b0\u4e0e\u62bd\u53d6\u5173\u952e\u6280\u672f\u7814\u7a76<\/p>\n<p>\u53f2\u5b66\u6587 \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2013 \u8f6c\u535a<br>\u9648\u6653\u9633 \u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb 2012\uff0c2015\u5e74\u6bd5\u4e1a \u5fae\u8f6f\u82cf\u5dde\u7814\u7a76\u9662\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u77ed\u6587\u672c\u8bed\u4e49\u76f8\u4f3c\u5ea6\u8ba1\u7b97<br>\u5f20\u745e\u7426&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2012&nbsp;\u89e3\u653e\u519b\u67d0\u90e8\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a\u57fa\u4e8e\u5173\u952e\u7279\u5f81\u805a\u7c7b\u7684Top N\u70ed\u70b9\u8bdd\u9898\u68c0\u6d4b\u65b9\u6cd5\u7814\u7a76<br>\u674e &nbsp;\u857e&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb\uff08\u8054\u5408\u8d75\u71d5\u5e73\u6559\u6388\uff09&nbsp;2012&nbsp;\u4e9a\u9a6c\u900a\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a<br>\u5b59\u68a6\u6dd1&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb\uff08\u8054\u5408\u5546\u5efa\u4e91\u6559\u6388\uff09&nbsp;2012&nbsp;\u652f\u4ed8\u5b9d\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a<br>\u738b &nbsp;\u7426 &nbsp;&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb\uff08\u8054\u5408\u5546\u5efa\u4e91\u6559\u6388\uff09&nbsp;2012&nbsp;\u592e\u884c\uff0c\u6bd5\u4e1a\u8bba\u6587\uff1a<\/p>\n<p>\u6731 &nbsp;\u5029 &nbsp;&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb\uff08\u8054\u5408\u5546\u5efa\u4e91\u6559\u6388\uff09&nbsp;2012&nbsp;\u5de5\u4fe1\u90e8\u7535\u5b50\u4e00\u6240<\/p>\n<p>\u674e &nbsp;\u7136&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2012 \u767e\u5ea6<\/p>\n<p>\u8d75\u5c0f\u5b9d&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb&nbsp;2011 \u534e\u4e3a<br>\u674e\u6e05\u654f&nbsp;\u9996\u90fd\u5e08\u5927 \u5ba2\u5ea7\u7855\u58eb&nbsp;2011 \u5de5\u4fe1\u90e8\u7535\u5b50\u4e00\u6240<br>\u674e\u7b11\u4f83&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb\uff08\u8054\u5408\u674e\u7389\u5c97\u535a\u58eb\uff09&nbsp;2010&nbsp;\u4e2d\u56fd\u4eba\u5bff<br>\u738b\u6653\u5189&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb\uff08\u8054\u5408\u8d75\u71d5\u5e73\u6559\u6388\uff09&nbsp;2010&nbsp;\u4eac\u4e1c\u65b9<br>\u6f58\u8fea&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb\uff08\u8054\u5408\u8d75\u71d5\u5e73\u6559\u6388\uff09&nbsp;2010&nbsp;\u65b0\u4e1c\u65b9<br>\u9ad8\u5065&nbsp;\u5317\u4eac\u7406\u5de5\u5927\u5b66 \u7855\u58eb\uff08\u8054\u5408\u8d75\u71d5\u5e73\u6559\u6388\uff09&nbsp;2009&nbsp;\u5317\u8f66\u96c6\u56e2<br>\u674e\u6052\u8bad&nbsp;\u9996\u90fd\u5e08\u5927 \u5ba2\u5ea7\u7855\u58eb&nbsp;2008&nbsp;\u516c\u5b89\u90e8\u4e00\u6240<br>\u5218\u6cbb\u534e&nbsp;\u5317\u65b9\u5de5\u4e1a\u5927\u5b66 \u5ba2\u5ea7\u7855\u58eb&nbsp;2008&nbsp;\u66d9\u5149\u516c\u53f8<br>\u848b\u9a88&nbsp;\u4e2d\u56fd\u79d1\u6280\u5927\u5b66 \u5ba2\u5ea7\u7855\u58eb&nbsp;2007&nbsp;\u5357\u4eac\u5de5\u4e1a\u5b66\u9662<br>\u5f20\u4eac\u9633&nbsp;\u9996\u90fd\u5e08\u5927 \u5ba2\u5ea7\u7855\u58eb&nbsp;2007&nbsp;\u7f51\u6613<br>\u79e6\u9e4f&nbsp;\u9996\u90fd\u5e08\u5927 \u5ba2\u5ea7\u7855\u58eb&nbsp;2007<\/p>\n<h2><strong>\u5b66\u672f\u8bba\u6587\u8bba\u8457\uff1a<\/strong><\/h2>\n<h3>\u3010\u51fa\u7248\u7684\u4e13\uff08\u8bd1\uff09\u8457\u3011<\/h3>\n<p>Huaping Zhang, Jianyun Shang. Natural Language Processing and Applications (2025), <a href=\"https:\/\/doi.org\/10.1007\/978-981-97-9739-4\">https:\/\/doi.org\/10.1007\/978-981-97-9739-4<\/a>, Springer(<\/p>\n<article data-content=\"[{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;AiCR-1744420142841&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{&quot;style&quot;:{},&quot;version&quot;:1},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;oPgi-1744420142840&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;978-981-97-9738-7&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:16}]}]}],&quot;state&quot;:{}}]\">\n<div>ISBN: 978-981-97-9738-7<span style=\"font-size: revert; color: initial;\">)<\/span><\/div>\n<\/article>\n<p>\u5f20\u534e\u5e73,\u5546\u5efa\u4e91,\u6c64\u6cfd\u9633\uff0c\u96f7\u6c9b\u94b6\u8457 \u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e0e\u5e94\u7528\uff082023\uff09,\u6e05\u534e\u5927\u5b66\u51fa\u7248\u793e\uff08ISBN:978-7-302-64626-6\uff09<\/p>\n<p>\u5f20\u534e\u5e73,\u5546\u5efa\u4e91\uff0c\u5218\u5146\u53cb\u7f16\u8457 \u5927\u6570\u636e\u667a\u80fd\u5206\u6790\uff082019\uff09,\u6e05\u534e\u5927\u5b66\u51fa\u7248\u793e\uff08ISBN:978-7-302-53117-3\uff09<br>\u5f20\u534e\u5e73,\u5546\u5efa\u4e91\uff0c\u767d\u7855\uff0c\u6bb5\u6c38\u671d\u7b49\u8457 \u5927\u6570\u636e\u5927\u5bb6\u8c08\uff082017.1\uff09,\u7535\u5b50\u5de5\u4e1a\u51fa\u7248\u793e\uff08ISBN:978-7-121-30181-0\uff09<br>\u5f20\u534e\u5e73,\u9ad8\u51ef\uff0c\u9ec4\u6cb3\u71d5,\u8d75\u71d5\u5e73.\u5927\u6570\u636e\u641c\u7d22\u4e0e\u6316\u6398.\u79d1\u5b66\u51fa\u7248\u793e.2014\u51fa\u7248\uff08ISBN:978-7-03-040318-6\uff09<br>\u5f20\u534e\u5e73,\u674e\u6052\u8bad,\u5218\u6cbb\u534e.\u4fe1\u606f\u68c0\u7d22\u7b97\u6cd5\u4e0e\u63a2\u7d22\u6cd5(\u8bd1\u8457)\uff0c\u4eba\u6c11\u90ae\u7535\u51fa\u7248\u793e\uff0c 2010\u5e749\u6708\uff08ISBN:978-7-115-23575-6\uff09<br>\u5218\u7fa4,\u5f20\u534e\u5e73,\u9a86\u536b\u534e,\u5b59\u5065\uff0c\u81ea\u7136\u8bed\u8a00\u7406\u89e3(\u8bd1\u8457), \u7535\u5b50\u5de5\u4e1a\u51fa\u7248\u793e, 2005\u5e741\u6708(ISBN\uff1a7-121-00755-X)<br>\u9ad8\u51ef\uff0c\u4ec7\u6676\uff0c\u5f20\u6653\u660e\uff0c\u738b\u4f1f\uff0c \u5f20\u534e\u5e73\uff0c\u4fe1\u606f\u68c0\u7d22\u4e0e\u667a\u80fd\u5904\u7406\uff0c\u56fd\u9632\u5de5\u4e1a\u51fa\u7248\u793e\uff0c2014\u3002<\/p>\n<h3>\u3010\u83b7\u5f97\u7684\u4e13\u5229\u3011<\/h3>\n<p>\u4e00\u79cd\u57fa\u4e8e\u60c5\u611f\u5c42\u6b21\u4f53\u7cfb\u7684\u60c5\u611f\u8bcd\u5178\u6784\u5efa\u65b9\u6cd5\uff08\u4e13\u5229\u53f7\uff1aZL201911233518.9\uff09\uff0c\u5f20\u5b9d\u534e\uff0c\u5f20\u534e\u5e73\uff0c\u5546\u5efa\u4e91\uff0c2019.12.5 \u5df2\u8f6c\u8ba9<\/p>\n<p>\u4e00\u79cd\u57fa\u4e8e\u77e5\u8bc6\u56fe\u8c31\u548c\u4e0a\u4e0b\u6587\u8bed\u5883\u7684\u4e2d\u6587\u8bed\u5883\u6d88\u6b67\u65b9\u6cd5\uff08\u4e13\u5229\u53f7\uff1aZL2021 1 0417960.8\uff09\uff0c\u5218\u5b50\u5b87\uff0c\u5f20\u534e\u5e73\uff0c2022.10.14<\/p>\n<p>\u4e00\u79cd\u83b7\u53d6\u591a\u5c42\u6b21\u4e0a\u4e0b\u6587\u8bed\u4e49\u7684\u6587\u672c\u5206\u7c7b\u65b9\u6cd5\uff08\u4e13\u5229\u53f7\uff1aZL201911246473.9\uff09\uff0c\u59dc\u5e86\u9e3f\uff0c\u5f20\u534e\u5e73\uff0c\u5546\u5efa\u4e91\uff0c2019.12.6<\/p>\n<p>\u5b8c\u7f8e\u53cc\u6570\u7ec4TRIE\u6811\u8bcd\u5178\u7ba1\u7406\u4e0e\u68c0\u7d22\u65b9\u6cd5(\u4e13\u5229\u53f7\uff1a200510130690.3)\uff0c\u5f20\u534e\u5e73\u3001\u738b\u601d\u529b\uff0c\u7b2c\u4e00\u53d1\u660e\u4eba<br>\u5468\u5efa\u680b\uff0c\u8d75\u71d5\u5e73\uff0c\u5f20\u534e\u5e73\uff0c\u674e\u60f3.\u4e00\u79cd\u7f51\u7edc\u4e2a\u4f53\u6216\u7fa4\u4f53\u60c5\u7eea\u8ba4\u77e5\u80fd\u529b\u9884\u6d4b\u4e0e\u53ef\u89c6\u5316\u65b9\u6cd5\uff08\u6388\u6743\u4e13\u5229\u53f7\uff1a 20140795679.8\uff09.\u4e13\u5229<br>\u5f20\u534e\u5e73\uff0c\u5468\u5efa\u680b\uff0c\u8d75\u71d5\u5e73\uff0c\u5b59\u68a6\u6dd1.\u4e00\u79cd\u7f51\u7edc\u4e2a\u4f53\u6216\u7fa4\u4f53\u4ef7\u503c\u89c2\u81ea\u52a8\u5224\u522b\u65b9\u6cd5.\u4e13\u5229\uff08\u5df2\u7533\u8bf7\u7acb\u9879\uff09<br>\u4e00\u79cd\u57fa\u4e8e\u5fae\u535a\u7279\u5b9a\u4e8b\u4ef6\u7684\u5f71\u54cd\u529b\u8ba1\u7b97\u65b9\u6cd5 ? ?\u4e13\u5229\u53f7\uff1a201610371596.5 \u7533\u8bf7\u65e5\uff1a2016.5.30<br>\u4e00\u79cd\u9762\u5411\u5fae\u535a\u7684\u7591\u4f3c\u6c34\u519b\u53d1\u73b0\u6280\u672f ? \u4e13\u5229\u53f7\uff1a201610371264.7?\u7533\u8bf7\u65e5\uff1a2016.5.30<\/p>\n<h3>\u3010\u83b7\u5f97\u7684\u8f6f\u4ef6\u8457\u4f5c\u6743\u3011<\/h3>\n<p>[1]\u8ba1\u7b97\u6240\u6c49\u8bed\u8bcd\u6cd5\u5206\u6790\u7cfb\u7edfICTCLAS\uff0c\u8f6f\u4ef6\u767b\u8bb0\u53f7\u4e3a2003SR0087\u3002<br>[2]\u6c49\u8bed\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u7cfb\u7edf\uff0c\u8f6f\u4ef6\u767b\u8bb0\u53f7\uff1a2004SR00677<\/p>\n<h3>\u3010\u53d1\u8868\u7684\u5176\u4ed6\u8bba\u6587\u3011<\/h3>\n<p>Ruohao Yan,, Huaping Zhang*,Karl. Research on Uyghur morphological segmentation based on long sequence labeling method,5th International Conference on Signal Processing and Machine Learning (SPML 2022), Dalin China, Aug. 2022 (Accepted)<\/p>\n<p>Asif Khan, Huaping Zhang*, Nada Boudjellal, Arshad Ahmad, Lin Dai, Jianyun Shang, Philipp Haindl, A Comparative Study Between Rule-Based and Transformer-Based Election Prediction Approaches: 2020 US Presidential Election as a Use Case, the 2nd International Workshop on AI System Engineering: Math, Modelling and Software (AISys2022), Vienna, Austria, Aug. 2022 (Accepted)<\/p>\n<p>\u5eb7\u94e0,\u5b8b\u82e5\u96e8,\u675c\u4f26,\u5f20\u534e\u5e73,\u90ed\u5b87\u822a\uff0c\u9762\u5411\u4e34\u5e8a\u53d1\u73b0\u7684\u590d\u6742\u4fe1\u606f\u62bd\u53d6,\u4e2d\u6587\u4fe1\u606f\u5b66\u62a5,2022(\u5df2\u5f55\u7528)<\/p>\n<p>\u5f20\u5b9d\u534e\uff0c\u674e\u6069\u6797\uff0c\u5f20\u534e\u5e73\uff0c\u57fa\u4e8e\u5c42\u6b21\u7ed3\u6784\u7684\u60c5\u611f\u5355\u5143\u8868\u793a\u65b9\u6cd5\uff0c\u8ba1\u7b97\u673a\u5de5\u7a0b\u4e0e\u79d1\u5b66.2021.&nbsp;<\/p>\n<p>Linfang Wu, Hua-Ping Zhang, Yaofei Yang and Kai Gao;Dynamic Prototype Selection by Fusing Attention Mechanism for Few Shot Relation Classification;12th Asian Conference on Intelligent Information and Database Systems. Thailand;2020.3<br>Yaofei Yang, Hua-Ping Zhang, Linfang Wu and Yangsen Zhang;Cached Embedding with Random Selection: Optimization Technique to Improve Training Speed of Character-Aware Embedding;12th Asian Conference on Intelligent Information and Database Systems. Thailand;2020.3<\/p>\n<p>Yang Y., Li S., Zhang Y., Zhang HP. (2019) Point the Point: Uyghur Morphological Segmentation Using PointerNetwork with GRU. In: Sun M., Huang X., Ji H., Liu Z., Liu Y. (eds) Chinese Computational Linguistics. CCL 2019. Lecture Notes in Computer Science, vol 11856. Springer, Cham \uff08SCI Indexed\uff09<\/p>\n<p>&#8220;Xi Zhang, Hua-Ping Zhang, Lei Zhao:<br>Reading More Efficiently: Multi-sentence Summarization with a Dual Attention and Copy-Generator Network. In: The Pacific Rim International Conferences on Artificial Intelligence, PRICAI 2018, Nanjing, Jiangsu, China, 28-31 August 2018, (1) 2018: 811-823&#8243;<br>Shen Gao, Huaping Zhang, Kai Gao. A Convolutional Neural Network Based Sentiment Classification and the Convolutional Kernel Representation[C]. Proceedings of 22nd International Conference on Natural Language &amp; Information Systems(NLDB), Springer LNCS, Li\u00e8ge, Belgium, 21-23 June, 2017, EI Compendex index<br>Shen Gao, Huaping Zhang, Kai Gao. Text Understanding with a Hybrid Neural Network Based Learning[C]. Proceedings of the 3th International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE), Springer LNCS, Changsha, China, 22-24 September, 2017, EI Compendex index.<br>SongZe Wu, Huaping Zhang, Chengcheng Xu, Tao Guo. Text Clustering on Short Message by Using Deep Semantic Representation. International Conference on Computational Sciences, Advanced Database and Computing, 2017.<br>&#8220;CHENGCHENG XU, HUAPING ZHANG, BINGBING LU AND SONGZE WU. Local<br>Community Detection Using Social Relations and Topic Features in Social Networks<br>[M]. Chinese Computational Linguistics and Natural Language Processing Based on<br>Naturally Annotated Big Data. Springer, Cham, 2017, 371-383.&#8221;<br>&#8220;BINGBING LU, HUAPING ZHANG, BIN LIU AND ZHONGHUA ZHAO. Research on<br>User Identification Algorithm Based on Massive Multi-site VPN Log [C]. IEEE, International Conference on Communication Technology. IEEE, 2017:1372-1381&#8243;<\/p>\n<p>A Convolutional Neural Network Based Sentiment Classification and the Convolutional Kernel Representation<br>News Abridgement Algorithm Based on Word Alignment and Syntactic Parsing[M]\/\/ Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. 2016.<\/p>\n<p>Qiao Y, Zhang Huaping(*), Yu M, et al. Sina-Weibo Spammer Detection with GBDT [M]\/\/ Social Media Processing. Springer Singapore, 2016.<br>Yang Qiao,Huaping Zhang*\uff08\u5f20\u534e\u5e73\uff09,.Effective Detecting Microblog Spammers Using Big Data Fusion Algorithm.WorldComp&#8217;16,July 25-28,Las Vegas,Nevada,USA<\/p>\n<p>Jiandong Zhou(\u5468\u5efa\u680b\uff09, Yanping Zhao\uff08\u8d75\u71d5\u5e73\uff09, Huaping Zhang*\uff08\u5f20\u534e\u5e73\uff09. Measuring Emotion Bifurcation Points for Individuals in Social Media[C].the Hawaii International Conference on System Science(\u56fd\u9645\u7cfb\u7edf\u79d1\u5b66\u5e74\u4f1a HICSS&#8217;49\uff0c\u7ba1\u7406A\u7c7b\u4f1a\u8bae, EI\u68c0\u7d22), Jan.5-8, 2016, Honolulu, KAUAI, Hawaii USA.2016:1949-1958.<\/p>\n<p>\u674e\u7136,\u5f20\u534e\u5e73,\u8d75\u71d5\u5e73\uff0c\u5546\u5efa\u4e91.\u57fa\u4e8e\u4e3b\u9898\u6a21\u578b\u4e0e\u4fe1\u606f\u71b5\u7684\u4e2d\u6587\u6587\u6863\u81ea\u52a8\u6458\u8981\u6280\u672f\u7814\u7a76(Automatic Text Summarization Research Based on Topic Model and Information Entropy).\u8ba1\u7b97\u673a\u79d1\u5b66.2014-11<\/p>\n<p>Kai Gao*, Hua-ping Zhang, Sheng-wang Li, Wei Wang, Jing Qiu;Research on Classification Algorithm and its Application in Cased-Based Reasoning,2014<\/p>\n<p>\u5f20\u534e\u5e73,\u4ee5\u4e3b\u4f53\u4e3a\u4e2d\u5fc3\u7684\u5fae\u535a\u8ba1\u7b97\u65b9\u6cd5,\u590d\u6742\u7cfb\u7edf\u4e0e\u590d\u6742\u6027\u79d1\u5b66,2012\u5e74\u7b2c\u56db\u671f p84-91<\/p>\n<p>Hua-Ping&nbsp;Zhang,Huan-Ping&nbsp;Wu,Jian&nbsp;Gao,Yan-Ping&nbsp;Zhao,Zhong-Liang&nbsp;Lv,Meteorological&nbsp;Bulletin&nbsp;Automatic&nbsp;Generation&nbsp;based&nbsp;on&nbsp;Spatio-Temporal&nbsp;Reasoning,In&nbsp;Proceedings&nbsp;of&nbsp;2011&nbsp;International&nbsp;Conference&nbsp;on&nbsp;Machine&nbsp;Learning&nbsp;and&nbsp;Cybernetics&nbsp;in&nbsp;Guangxi,&nbsp;China,2011.7\uff0cp1927-1931<\/p>\n<p>Hua-Ping Zhang, Qian Mo,He-Yang Huang,Structured POI data Extraction from Internet News,In Proceedings of the 4th International Universal Communication Symposium (IUCS 2010) in Beijing, China,2010.10\uff0cp115-120(\u7279\u9080\u62a5\u544a)<br>Hua-Ping ZHANG,Jian GAO,Qian MO, He-Yan HUANG. Incorporating New Words Detection with Chinese Word Segmentation. In Proceedings of CIPS-SIGHAN Joint Conference on Chinese Language Processing (CLP 2010).Beijing, China.2010.8 .p249-251.<br>Hua-Ping ZHANG,Zhi-Hua LIU,Qian MO,He-Yan HUANG. Chinese Personal Name Disambiguation Based on Person Modeling. In Proceedings of CIPS-SIGHAN Joint Conference on Chinese Language Processing (CLP 2010).Beijing, China.2010.8 .p374-378<\/p>\n<p>Hua-Ping Zhang, Hong-Bo Xu, Shuo Bai, Bin Wang, Xue-Qi Cheng. Experiments in TREC 2004 Novelty Track at CAS-ICT. In Proc. of the 13th Text Retrieval Conference, Gaithersburg, Maryland, November, 2004, pp287<\/p>\n<p>Hua-Ping ZHANG, Qun LIU, Xue-Qi CHENG, Hao Zhang, Hong-Kui Yu. Chinese Lexical Analysis Using Hierarchical Hidden Markov Model, Second SIGHAN workshop affiliated with 41st ACL; Sapporo Japan, July, 2003, pp. 63-70<br>&nbsp;Hua-Ping ZHANG, Hong-Kui Yu, De-Yi Xiong, Qun LIU. HHMM-based Chinese Lexical Analyzer ICTCLAS, Second SIGHAN workshop affiliated with 41th ACL; Sapporo Japan, July, 2003, pp. 184-187<br>&nbsp;Kevin Zhang (Hua-Ping Zhang), Qun Liu, Hao Zhang, Xueqi Cheng. Automatic Recognition of Chinese Unknown Words Based on Role Tagging, First SIGHAN affiliated with 19th COLING, September 2002, pp71-77<\/p>\n<\/div>\n<p><\/p>\n<p class=\"wp-block-paragraph\"><\/p>\n<p class=\"wp-block-paragraph\"><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>CV in English Version \u5f20\u534e\u5e73 \u7279\u8058\u6559\u6388&nbsp; \u535a\u58eb\u751f &hellip; <a href=\"http:\/\/www.nlpir.org\/wordpress\/2021\/11\/30\/drkevinzhang-cn\/\">\u7ee7\u7eed\u9605\u8bfb <span 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id=\\\"activity-name\\\" class=\\\"rich_media_title \\\"><a href=\\\"https:\\\/\\\/mp.weixin.qq.com\\\/s\\\/bZhuLDDElViPHI66ZlJmBw\\\">\\u5148\\u8fdb\\uff01\\u4eca\\u5929\\uff0c\\u4ed6\\u4eec\\u83b7\\u8868\\u5f70\\uff01<\\\/a><br><a href=\\\"https:\\\/\\\/mp.weixin.qq.com\\\/s\\\/OPf8c4306c89-eQnCTxDuw\\\">\\u300a\\u4eba\\u6c11\\u65e5\\u62a5\\u300b\\u62a5\\u9053\\u5317\\u7406\\u5de5\\u201c\\u667a\\u6167\\u601d\\u653f\\u201d\\uff0c\\u79d1\\u6280\\u611f\\u6d53\\u6d53\\uff01<\\\/a><\\\/p>\\n<p id=\\\"activity-name\\\" class=\\\"rich_media_title \\\"><a href=\\\"https:\\\/\\\/mp.weixin.qq.com\\\/s\\\/zk9QyzIMJim03CFoDVXMZw\\\">\\u5f20\\u534e\\u5e73\\uff1a\\u8eac\\u8015\\u6559\\u575b\\uff0c\\u57f9\\u517b\\u9886\\u519b\\u4eba\\u624d<\\\/a><\\\/p>\\n<h2><strong>\\u4ee3\\u8868\\u6027\\u8bba\\u6587\\uff1a<\\\/strong><\\\/h2>\\n<p>Wenyao Cui, Huaping Zhang; SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification; ACM KDD2026, &nbsp;Jeju, Korea August 9-13,&nbsp;<em>2026<\\\/em><\\\/p>\\n<p>Yunhan Bu, Huaping Zhang; Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization, ICML 2026, Seoul, Korea July 6\\u201311, <em>2026<\\\/em><\\\/p>\\n<p>Haocheng Lv, Huaping Zhang; Improving Multimodal Reasoning via Worst Dimension Optimization,IJCAI-ECAI 2026, the 35th International Joint Conference on Artificial Intelligence, Bremen, Germany, on August 15-21, 2026<\\\/p>\\n<h3><a title=\\\"\\\" href=\\\"https:\\\/\\\/openreview.net\\\/profile?id=~Yinuo_Wang16\\\" data-toggle=\\\"tooltip\\\" data-placement=\\\"top\\\" data-original-title=\\\"~Yinuo_Wang16\\\">Yinuo Wang<\\\/a>,&nbsp;<a title=\\\"\\\" href=\\\"https:\\\/\\\/openreview.net\\\/profile?id=~Qingjie_Li1\\\" data-toggle=\\\"tooltip\\\" data-placement=\\\"top\\\" data-original-title=\\\"~Qingjie_Li1\\\">Qingjie Li<\\\/a>,&nbsp;<a title=\\\"\\\" href=\\\"https:\\\/\\\/openreview.net\\\/profile?id=~Wenyao_Cui1\\\" data-toggle=\\\"tooltip\\\" data-placement=\\\"top\\\" data-original-title=\\\"~Wenyao_Cui1\\\">Wenyao Cui<\\\/a>,&nbsp;<a title=\\\"\\\" href=\\\"https:\\\/\\\/openreview.net\\\/profile?id=~Qiuchi_Li1\\\" data-toggle=\\\"tooltip\\\" data-placement=\\\"top\\\" data-original-title=\\\"~Qiuchi_Li1\\\">Qiuchi Li<\\\/a>,&nbsp;<a title=\\\"\\\" href=\\\"https:\\\/\\\/openreview.net\\\/profile?id=~Zhang_Huaping1\\\" data-toggle=\\\"tooltip\\\" data-placement=\\\"top\\\" data-original-title=\\\"~Zhang_Huaping1\\\">Zhang Huaping<\\\/a>&nbsp;,2026.ConMA : Confidence-Guided Kernel Sampling with Multi-Stage Aggregation for LLM Reasoning,<span id=\\\"citeACL\\\">In&nbsp;<i>Findings of the Association for Computational Linguistics ACL 2026<\\\/i>, . Association for Computational Linguistics.<\\\/span><button class=\\\"btn btn-clipboard btn-secondary btn-sm ml-2\\\" type=\\\"button\\\" data-clipboard-target=\\\"#citeACL\\\"><i class=\\\"far fa-clipboard\\\"><\\\/i><\\\/button> (CCF A top conference)<\\\/h3>\\n<p>Yelin Chen, Huaping Zhang, Ruohao Yan, Jihong Zhu, Askar Hamdulla,LiteNER: A novel lightweight method for long text named entity recognition,Information Processing &amp; Management,Volume 64, Issue 1, Part A,2027,104981,ISSN 0306-4573,https:\\\/\\\/doi.org\\\/10.1016\\\/j.ipm.2026.104981.<br>(https:\\\/\\\/www.sciencedirect.com\\\/science\\\/article\\\/pii\\\/S0306457326003729)<\\\/p>\\n<p>Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li ,2026.BCL: Bayesian In-Context Learning Framework for Information Extraction,<span id=\\\"citeACL\\\">In&nbsp;<i>Findings of the Association for Computational Linguistics ACL 2026<\\\/i>, . Association for Computational Linguistics.<\\\/span><button class=\\\"btn btn-clipboard btn-secondary btn-sm ml-2\\\" type=\\\"button\\\" data-clipboard-target=\\\"#citeACL\\\"><i class=\\\"far fa-clipboard\\\"><\\\/i><\\\/button> (CCF A top conference)<\\\/p>\\n<p>Sen Jia, Huayu Wang, Hsiang-Wei Huang, Zhaochong An, Jenq-Neng Hwang, Zhang Huaping, Lei Li ,2026\\uff0cCLEP: Contrastive Language-Pose Pretraining<br>CVPR2026<\\\/p>\\n<p>Sen Jia, Ning Zhu, Jinqin Zhong, Jiale Zhou, Zhang Huaping, Jenq-Neng Hwang, Lei Li ,2026,RAM: Recover Any 3D Human Motion in-the-Wild<br>,CVPR2026<\\\/p>\\n<p><span id=\\\"citeACL\\\">Linhan Li and Zhang Huaping. 2024.&nbsp;<a href=\\\"https:\\\/\\\/aclanthology.org\\\/2024.findings-acl.249\\\">Context Length Extension via Generalized Extrapolation Scale<\\\/a>. In&nbsp;<i>Findings of the Association for Computational Linguistics ACL 2024<\\\/i>, pages 4211\\u20134218, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.<\\\/span><button class=\\\"btn btn-clipboard btn-secondary btn-sm ml-2\\\" type=\\\"button\\\" data-clipboard-target=\\\"#citeACL\\\"><i class=\\\"far fa-clipboard\\\"><\\\/i><\\\/button> (CCF A top conference)<\\\/p>\\n<p><i>Zichao Lin, Shuyan Guan, Wending Zhang, Huiyan Zhang, Yugang Li &amp; Huaping Zhang.<\\\/i>&nbsp;<a href=\\\"https:\\\/\\\/doi.org\\\/10.1007\\\/s10462-024-10896-y\\\">Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models<\\\/a>.&nbsp;<i>Artificial Intelligence Review<\\\/i>&nbsp;<b>57<\\\/b>, 243 (2024). https:\\\/\\\/doi.org\\\/10.1007\\\/s10462-024-10896-y (SCI Q1, \\u4e2d\\u79d1\\u96622\\u533a\\uff0cImpactor Factor: 11.7)<\\\/p>\\n<p>Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang*,STMAP: A novel semantic text matching model augmented with embedding perturbations,<br>Information Processing &amp; Management,Volume 61, Issue 1,2024,103576,ISSN 0306-4573,https:\\\/\\\/doi.org\\\/10.1016\\\/j.ipm.2023.103576.<br>(https:\\\/\\\/www.sciencedirect.com\\\/science\\\/article\\\/pii\\\/S0306457323003138\\uff0cSCI \\u4e2d\\u79d1\\u96621\\u533a\\uff0cImpactor Factor: 8.6\\uff0c\\u9876\\u520a)<\\\/p>\\n<p>Baohua Zhang, Jiahao Cai, Huaping Zhang*, Jianyun Shang,VisPhone: Chinese named entity recognition model enhanced by visual and phonetic features,<em><i>Information Processing &amp; Management<\\\/i><\\\/em>,Volume 60, Issue 3,2023,103314,ISSN 0306-4573,<a href=\\\"https:\\\/\\\/doi.org\\\/10.1016\\\/j.ipm.2023.103314\\\">https:\\\/\\\/doi.org\\\/10.1016\\\/j.ipm.2023.103314<\\\/a>.(<a href=\\\"https:\\\/\\\/www.sciencedirect.com\\\/science\\\/article\\\/pii\\\/S0306457323000511\\\">https:\\\/\\\/www.sciencedirect.com\\\/science\\\/article\\\/pii\\\/S0306457323000511<\\\/a>) (SCI \\u4e2d\\u79d1\\u96621\\u533a\\uff0cImpactor Factor:8.6\\uff0c\\u9876\\u520a)<\\\/p>\\n<p>Baohua Zhang, Yongyi Huang, Wenyao Cui, Zhang Huaping, and Jianyun Shang. 2023. PsyAttention: Psychological Attention Model for Personality Detection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3398-3411, Singapore. Association for Computational Linguistics.<\\\/p>\\n<article data-content=\\\"[{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;37Ms-1765280017769&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{&quot;version&quot;:1},&quot;nodes&quot;:[{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;L3Y1-1768821497678&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\\\/\\\/dblp.org\\\/pid\\\/409\\\/8575.html&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;ipZd-1768821497683&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Longyi&nbsp;Ye&quot;,&quot;marks&quot;:[]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;RmAp-1768821497685&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;,&nbsp;Huaping&nbsp;Zhang:&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#505b62&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:16}]},{&quot;text&quot;:&quot;LLMProto:&nbsp;A&nbsp;Hardware-Efficient&nbsp;Finetuning&nbsp;Model&nbsp;for&nbsp;Few-Shot&nbsp;Relation&nbsp;Extraction&nbsp;with&nbsp;Large&nbsp;Language&nbsp;Model.&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;bold&quot;},{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#666666&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:16}]},{&quot;text&quot;:&quot;&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#505b62&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:16}]}]},{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;D4zY-1768821497681&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\\\/\\\/dblp.org\\\/db\\\/conf\\\/icassp\\\/icassp2025.html#YeZ25&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;6Oy4-1768821497688&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;ICASSP&nbsp;2025&quot;,&quot;marks&quot;:[]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;NKIH-1768821497689&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;:&nbsp;1-5&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#505b62&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:16}]}]}],&quot;state&quot;:{}}]\\\">\\n<div><a href=\\\"https:\\\/\\\/dblp.org\\\/pid\\\/409\\\/8575.html\\\">Longyi Ye<\\\/a>,&nbsp;Huaping Zhang: LLMProto: A Hardware-Efficient Finetuning Model for Few-Shot Relation Extraction with Large Language Model.&nbsp;<a href=\\\"https:\\\/\\\/dblp.org\\\/db\\\/conf\\\/icassp\\\/icassp2025.html#YeZ25\\\">ICASSP&nbsp;2025<\\\/a>:&nbsp;1-5<\\\/div>\\n<\\\/article>\\n<p>R. Yan, H. Zhang*, W. Silamu and A. Hamdulla, \\\"Unsupervised word Segmentation Based on Word Influence,\\\"&nbsp;<em>ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)<\\\/em>, Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109\\\/ICASSP49357.2023.10096718. (\\u4fe1\\u53f7\\u5904\\u7406\\u56fd\\u9645\\u9876\\u4f1a)<\\\/p>\\n<p>Wenyao Cui, Jiahao Cai, Baohua Zhang, Yongyi Huang, Huaping Zhang*, \\\"BRIDGING THE GAP: A SELF-LEARNING MODEL USING IMPLICIT KNOWLEDGE FOR CHINESE SPELLING CORRECTION\\\"&nbsp;<em>ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)<\\\/em>, Seoul, Korea,&nbsp; 2024, pp. 1-5, doi:&nbsp; &nbsp;( \\u4fe1\\u53f7\\u5904\\u7406\\u56fd\\u9645\\u9876\\u4f1a)<\\\/p>\\n<article data-content=\\\"[{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;hbgV-1766639447322&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;rGLo-1766639447320&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Geleta&nbsp;Negasa&nbsp;Binegde,&nbsp;Huaping&nbsp;Zhang,Exploring&nbsp;cultural&nbsp;commonsense&nbsp;in&nbsp;multilingual&nbsp;large&nbsp;language&nbsp;models:&nbsp;A&nbsp;survey,Information&nbsp;Systems,Volume&nbsp;138,2026,102649,ISSN&nbsp;0306-4379,&quot;,&quot;marks&quot;:[]}]},{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;2XVL-1765280017774&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\\\/\\\/doi.org\\\/10.1016\\\/j.is.2025.102649.&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;OqEZ-1765280017773&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;https:\\\/\\\/doi.org\\\/10.1016\\\/j.is.2025.102649.&quot;,&quot;marks&quot;:[]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;UWCP-1765280017776&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;\\u3001&quot;,&quot;marks&quot;:[]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;37Ms-1765280017769&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{&quot;version&quot;:1},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;qyVh-1765280017770&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Brahimi,&nbsp;Nihad,&nbsp;Huaping&nbsp;Zhang,&nbsp;and&nbsp;Zahid&nbsp;Razzaq.&nbsp;2025.&nbsp;\\\\&quot;Quantum-Inspired&nbsp;Spatio-Temporal&nbsp;Inference&nbsp;Network&nbsp;for&nbsp;Sustainable&nbsp;Car-Sharing&nbsp;Demand&nbsp;Prediction\\\\&quot;&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]},{&quot;text&quot;:&quot;Sustainability&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;},{&quot;type&quot;:&quot;italic&quot;}]},{&quot;text&quot;:&quot;&nbsp;17,&nbsp;no.&nbsp;11:&nbsp;4987.&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]},{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;xX4v-1748522354425&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\\\/\\\/doi.org\\\/10.3390\\\/su17114987&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;3z0O-1748522354424&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;https:\\\/\\\/doi.org\\\/10.3390\\\/su17114987&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;}]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;5fIY-1748522354426&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;8Ea1-1748522352080&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;SeHe-1748522352078&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]}],&quot;state&quot;:{}}]\\\">\\n<div>Geleta Negasa Binegde, Huaping Zhang,Exploring cultural commonsense in multilingual large language models: A survey,Information Systems,Volume 138,2026,102649,ISSN 0306-4379,<a href=\\\"https:\\\/\\\/doi.org\\\/10.1016\\\/j.is.2025.102649.\\\">https:\\\/\\\/doi.org\\\/10.1016\\\/j.is.2025.102649.<\\\/a><\\\/div>\\n<div>&nbsp;<\\\/div>\\n<\\\/article>\\n<p>Brahimi, Nihad, Huaping Zhang, and Zahid Razzaq. 2025. \\\"Quantum-Inspired Spatio-Temporal Inference Network for Sustainable Car-Sharing Demand Prediction\\\"&nbsp;<em>Sustainability<\\\/em>&nbsp;17, no. 11: 4987. https:\\\/\\\/doi.org\\\/10.3390\\\/su17114987<\\\/p>\\n<p>Brahimi, N.; Zhang, H.; Zaidi, S.D.A.; Dai, L. A Unified Spatio-Temporal Inference Network for Car-Sharing Serial Prediction.&nbsp;<em>Sensors<\\\/em>&nbsp;<b>2024<\\\/b>,&nbsp;<em>24<\\\/em>, 1266. <a href=\\\"https:\\\/\\\/doi.org\\\/10.3390\\\/s24041266\\\">https:\\\/\\\/doi.org\\\/10.3390\\\/s24041266<\\\/a>&nbsp; <span style=\\\"font-size: inherit;\\\"> ( SCI indexed, JCR Q2,\\u4e2d\\u79d1\\u9662\\u533a3\\u533a Impactor Factor: 3.9)<\\\/span><\\\/p>\\n<p>Zhang B, Zhang H*, Shang J and Cai J (2022) <a href=\\\"http:\\\/\\\/journal.frontiersin.org\\\/article\\\/10.3389\\\/fnbot.2022.897402\\\/full?&amp;utm_source=Email_to_authors_&amp;utm_medium=Email&amp;utm_content=T1_11.5e1_author&amp;utm_campaign=Email_publication&amp;field=&amp;journalName=Frontiers_in_Neurorobotics&amp;id=897402\\\">An Augmented Neural Network for Sentiment Analysis Using Grammar<\\\/a>.&nbsp;<i>Front. Neurorobot.<\\\/i> 16:897402. doi: 10.3389\\\/fnbot.2022.897402 <span style=\\\"font-size: inherit;\\\"> ( SCI indexed, JCR Q2,\\u4e2d\\u79d1\\u9662\\u533a3\\u533a Impactor Factor: 3.493)<\\\/span><\\\/p>\\n<p><span style=\\\"font-size: revert; color: initial;\\\">Li, L.H., Zhang, H.P., Li, C.J., et al.<\\\/span><a style=\\\"font-size: revert;\\\" href=\\\"https:\\\/\\\/direct.mit.edu\\\/dint\\\/article\\\/doi\\\/10.1162\\\/dint_a_00232\\\/117502\\\/Evaluation-on-ChatGPT-for-Chinese-Language\\\">: Evaluation on ChatGPT for Chinese Language Understanding<\\\/a><span style=\\\"font-size: revert; color: initial;\\\">. Data Intelligence&nbsp;<\\\/span>5(4) (2023).1-19 doi: https:\\\/\\\/doi.org\\\/10.1162\\\/dint_a_00232&nbsp; (SCI \\u4e09\\u533a)<\\\/p>\\n<p>A. Khan, N. Boudjellal, Huaping Zhang*, A. Ahmad, M. Khan, <a href=\\\"https:\\\/\\\/doi.org\\\/10.32604\\\/cmc.2023.44403\\\">From Social Media to Ballot Box: Leveraging Location-Aware Sentiment Analysis for Election Predictions<\\\/a>, Computers, Materials &amp; Continua. 2023. https:\\\/\\\/doi.org\\\/10.32604\\\/cmc.2023.44403. (SCIE) .<br>A. Khan, Huaping Zhang*, N. Boudjellal, A. Ahmad, M. Khan,<a href=\\\"https:\\\/\\\/doi.org\\\/10.32604\\\/cmc.2023.041520\\\"> Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model<\\\/a>, Computers, Materials &amp; Continua. 2023. https:\\\/\\\/doi.org\\\/10.32604\\\/cmc.2023.041520. (SCIE)<\\\/p>\\n<p><span style=\\\"font-size: inherit;\\\">Yang, Z., Zhang, H., Shang, J., Wushour, S. (2022). An Enhanced New Word Identification Approach Using Bilingual Alignment. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13551. Springer, Cham. https:\\\/\\\/doi.org\\\/10.1007\\\/978-3-031-17120-8_8<br>Kang, K., Zhang, H., Li, Y., Luo, X., Wushour, S. (2022). Automatic Academic Paper Rating Based on?Modularized Hierarchical Attention Network. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13551. Springer, Cham. https:\\\/\\\/doi.org\\\/10.1007\\\/978-3-031-17120-8_52<\\\/span><\\\/p>\\n<p>\\u5f20\\u534e\\u5e73, \\u674e\\u6797\\u7ff0, \\u674e\\u6625\\u9526.&nbsp;ChatGPT\\u4e2d\\u6587\\u6027\\u80fd\\u6d4b\\u8bc4\\u4e0e\\u98ce\\u9669\\u5e94\\u5bf9*[J]. \\u6570\\u636e\\u5206\\u6790\\u4e0e\\u77e5\\u8bc6\\u53d1\\u73b0, 2023, 0(0): 1-10 <a href=\\\"https:\\\/\\\/mp.weixin.qq.com\\\/s\\\/_yIDKHwhSHflxyuDUcqr-g\\\">doi:10.11925\\\/infotech.2096-3467.2023.0214<\\\/a><\\\/p>\\n<p>\\u5f20\\u534e\\u5e73, \\u674e\\u6797\\u7ff0, \\u674e\\u6625\\u9526.&nbsp;ChatGPT\\u4e2d\\u6587\\u7406\\u89e3\\u80fd\\u529b\\u6d4b\\u8bc4\\u4e0e\\u98ce\\u9669\\u5e94\\u5bf9\\u7b56\\u7565\\u7814\\u7a76*[J]. \\u4e2d\\u56fd\\u4eba\\u5de5\\u667a\\u80fd\\u5b66\\u4f1a\\u901a\\u8baf, 2023, 13(5): 46-52&nbsp;<\\\/p>\\n<article data-content=\\\"[{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;DwtO-1744372426873&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;0sxP-1744372426874&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Brahimi,&nbsp;Nihad,&nbsp;Huaping&nbsp;Zhang,&nbsp;and&nbsp;Zahid&nbsp;Razzaq.&nbsp;2025.&nbsp;\\\\&quot;Quantum-Inspired&nbsp;Spatio-Temporal&nbsp;Inference&nbsp;Network&nbsp;for&nbsp;Sustainable&nbsp;Car-Sharing&nbsp;Demand&nbsp;Prediction\\\\&quot;&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]},{&quot;text&quot;:&quot;Sustainability&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;},{&quot;type&quot;:&quot;italic&quot;}]},{&quot;text&quot;:&quot;&nbsp;17,&nbsp;no.&nbsp;11:&nbsp;4987.&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]},{&quot;type&quot;:&quot;inline&quot;,&quot;id&quot;:&quot;xX4v-1748522354425&quot;,&quot;name&quot;:&quot;link&quot;,&quot;data&quot;:{&quot;href&quot;:&quot;https:\\\/\\\/doi.org\\\/10.3390\\\/su17114987&quot;},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;3z0O-1748522354424&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;https:\\\/\\\/doi.org\\\/10.3390\\\/su17114987&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;}]}]}]},{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;5fIY-1748522354426&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]}]}],&quot;state&quot;:{}},{&quot;type&quot;:&quot;block&quot;,&quot;id&quot;:&quot;8Ea1-1748522352080&quot;,&quot;name&quot;:&quot;paragraph&quot;,&quot;data&quot;:{},&quot;nodes&quot;:[{&quot;type&quot;:&quot;text&quot;,&quot;id&quot;:&quot;SeHe-1748522352078&quot;,&quot;leaves&quot;:[{&quot;text&quot;:&quot;Brahimi,&nbsp;N.,&nbsp;Zhang,&nbsp;H.,&nbsp;&amp;&nbsp;Razzaq,&nbsp;Z.&nbsp;(2025).&nbsp;Explainable&nbsp;Spatio-Temporal&nbsp;Inference&nbsp;Network&nbsp;for&nbsp;Car-Sharing&nbsp;Demand&nbsp;Prediction.&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]},{&quot;text&quot;:&quot;ISPRS&nbsp;International&nbsp;Journal&nbsp;of&nbsp;Geo-Information&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;},{&quot;type&quot;:&quot;italic&quot;}]},{&quot;text&quot;:&quot;,&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;}]},{&quot;text&quot;:&quot;14&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&quot;,&quot;value&quot;:&quot;#222222&quot;},{&quot;type&quot;:&quot;backgroundColor&quot;,&quot;value&quot;:&quot;rgb(255,&nbsp;255,&nbsp;255)&quot;},{&quot;type&quot;:&quot;fontSize&quot;,&quot;value&quot;:12},{&quot;type&quot;:&quot;fontFamily&quot;,&quot;value&quot;:&quot;Arial&quot;},{&quot;type&quot;:&quot;italic&quot;}]},{&quot;text&quot;:&quot;(4),&nbsp;163.&nbsp;&quot;,&quot;marks&quot;:[{&quot;type&quot;:&quot;color&qu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Nihad, Huaping Zhang, and Zahid Razzaq. 2025. \\\"Quantum-Inspired Spatio-Temporal Inference Network for Sustainable Car-Sharing Demand Prediction\\\"&nbsp;Sustainability&nbsp;17, no. 11: 4987. <a href=\\\"https:\\\/\\\/doi.org\\\/10.3390\\\/su17114987\\\">https:\\\/\\\/doi.org\\\/10.3390\\\/su17114987<\\\/a><\\\/div>\\n<div>Brahimi, N., Zhang, H., &amp; Razzaq, Z. (2025). 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Khan, H. Zhang, N. Boudjellal, B. Hayat, L. Dai, A. Ahmad, A. Al-Hamed, Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media, in: International Conference on Information Technology and Applications. 16th ICITA 2022, Springer, Cham, 2023, pp. 163\\u2013173. doi: 10.1007\\\/978-981-19-9331-2_14. (EI\\\/ISI\\\/SCOPUS) <br>A. Khan, H. Zhang, N. Boudjellal, L. Dai, A. Ahmad, J. Shang, P. Haindl, A Comparative Study Between Rule-Based and Transformer-Based Election Prediction Approaches: 2020 US Presidential Election as a Use Case, in: Database Expert Syst. Appl. \\u2013 33rd DEXA 2022, Springer, Cham, 2022: pp. 32\\u201343. http:\\\/\\\/doi.org\\\/10.1007\\\/978-3-031-14343-4_4. (CCF\\\/EI\\\/ISI\\\/SCOPUS)<\\\/p>\\n<p>\\u5f20\\u534e\\u5e73,\\u5f20\\u5b9d\\u534e,\\u674e\\u5fd7\\u5f3a, \\u6768\\u8513\\u829d,\\u6768\\u5b50\\u7814\\uff0c\\u4e25\\u82e5\\u8c6a.\\u57fa\\u4e8e\\u8bcd\\u4e32\\u7684\\u5c0f\\u8bed\\u79cd\\u9884\\u8bad\\u7ec3\\u8bed\\u8a00\\u6a21\\u578b\\u53ca\\u8bed\\u8a00\\u5206\\u6790\\u6280\\u672f[J]. \\u4e2d\\u56fd\\u4eba\\u5de5\\u667a\\u80fd\\u5b66\\u4f1a\\u901a\\u8baf, 2022, 3: 16-21.<\\\/p>\\n<p>\\u5f20\\u5b9d\\u534e, \\u5f20\\u534e\\u5e73, \\u5389\\u94c1\\u5e05, \\u5546\\u5efa\\u4e91. \\u57fa\\u4e8e\\u591a\\u8f93\\u5165\\u6a21\\u578b\\u53ca\\u53e5\\u6cd5\\u7ed3\\u6784\\u7684\\u4e2d\\u6587\\u8bc4\\u8bba\\u60c5\\u611f\\u5206\\u6790\\u65b9\\u6cd5[J]. \\u5927\\u6570\\u636e, 2021, 7(6): 41-52.<\\\/p>\\n<p>\\u5f20\\u534e\\u5e73 \\u5f20\\u82af\\u94ed \\u5434\\u6797\\u82b3 \\u674e\\u660c\\u8d6b \\u5546\\u5efa\\u4e91;\\u5c0f\\u6837\\u672c\\u77e5\\u8bc6\\u56fe\\u8c31\\u7684\\u6784\\u5efa\\u4e0e\\u5e94\\u7528;\\u4eba\\u5de5\\u667a\\u80fd;2020\\u5e74\\u7b2c2\\u671f,p114-126<\\\/p>\\n<p>Huaping Zhang1, Jun Miao2, Ziyu Liu1, Ian Logan Wesson1, Jianyun Shang1;NLPIR-Parser: Making Chinese and English Semantic Analysis Easier and Complete;15th International Conference on the Statistical Analysis of Textual Data;France\\uff0c 2020.8&nbsp;<\\\/p>\\n<p>\\u5f20\\u534e\\u5e73\\uff0c\\u5546\\u5efa\\u4e91.<a href=\\\"http:\\\/\\\/www.nlpir.org\\\/wordpress\\\/wp-content\\\/uploads\\\/2019\\\/09\\\/\\u5f20\\u534e\\u5e73\\u3001\\u5546\\u5efa\\u4e91_NLPIR-Parser\\u5927\\u6570\\u636e\\u8bed\\u4e49\\u667a\\u80fd\\u5206\\u6790\\u5e73\\u53f0.pdf\\\">NLPIR-Parser\\uff1a\\u5927\\u6570\\u636e\\u8bed\\u4e49\\u667a\\u80fd\\u5206\\u6790\\u5e73\\u53f0<\\\/a>.\\u8bed\\u6599\\u5e93\\u8bed\\u8a00\\u5b66.2019\\u5e74\\u7b2c6\\u5377\\u7b2c1\\u671f\\uff0c87-104. 2019\\u5e749\\u6708<\\\/p>\\n<p>\\u5f20\\u534e\\u5e73\\uff0c\\u5546\\u5efa\\u4e91.\\u9762\\u5411\\u793e\\u4f1a\\u5a92\\u4f53\\u7684\\u5f00\\u653e\\u9886\\u57df\\u65b0\\u8bcd\\u53d1\\u73b0.\\u4e2d\\u6587\\u4fe1\\u606f\\u5b66\\u62a5.\\u7b2c31\\u5377\\u7b2c3\\u671f\\uff0c115-121. 2017\\u5e745\\u6708<br>\\u5f20\\u534e\\u5e73\\uff0c\\u674e\\u6052\\u8bad\\uff0c\\u674e\\u6e05\\u654f. \\u60c5\\u611f\\u8bcd\\u53d1\\u73b0\\u4e0e\\u6781\\u6027\\u6743\\u91cd\\u81ea\\u52a8\\u8ba1\\u7b97\\u7b97\\u6cd5\\u7814\\u7a76.\\u4e2d\\u6587\\u4fe1\\u606f\\u5b66\\u62a5.\\u7b2c31\\u5377\\u7b2c3\\u671f\\uff0c60-66. 2017\\u5e745\\u6708<\\\/p>\\n<p>\\u5f20\\u534e\\u5e73,\\u5b59\\u68a6\\u59dd,\\u5f20\\u745e\\u7426,\\u674e\\u857e.\\u5fae\\u535a\\u535a\\u4e3b\\u7684\\u7279\\u5f81\\u4e0e\\u884c\\u4e3a\\u5927\\u6570\\u636e\\u6316\\u6398.\\u4e2d\\u56fd\\u8ba1\\u7b97\\u673a\\u5b66\\u4f1a\\u901a\\u8baf.2014.6.p36-p43<\\\/p>\\n<p>Huaping&nbsp;Zhang,Ruiqi&nbsp;Zhang,Yanping&nbsp;Zhao,Baojun&nbsp;Ma;Big&nbsp;Data&nbsp;Modeling&nbsp;and&nbsp;Analysis&nbsp;of&nbsp;Microblog&nbsp;Ecosystem;International&nbsp;Journal&nbsp;of&nbsp;Automation&nbsp;and&nbsp;Computing,2014.11(2)&nbsp;p119-127<\\\/p>\\n<p>Baojun Ma,Huaping Zhao,Guoqing Chen,Yanping Zhao;Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective;Int'l Journal of Software Engineering and Knowledge Engineering,2013\\uff08 SCI Indexed\\uff09<\\\/p>\\n<p>Hua-Ping ZHANG, Jian Sun, Bin WANG, Shuo BAI. Computation on Sentence Semantic Distance for Novelty Detection; Chinese Journal of Computer Science and Tech. vol.3, 2005\\uff08 SCI Indexed\\uff09<\\\/p>\\n<p>\\u5f20\\u534e\\u5e73,\\u5218\\u7fa4. \\u57fa\\u4e8e\\u89d2\\u8272\\u6807\\u6ce8\\u7684\\u4e2d\\u56fd\\u4eba\\u540d\\u81ea\\u52a8\\u8bc6\\u522b\\u7814\\u7a76. \\u8ba1\\u7b97\\u673a\\u5b66\\u62a5, vol.27, No.1, 2004, pp.85-91&nbsp;&nbsp; \\u88ab\\u5f15\\u752831\\u6b21<br>\\u5218\\u7fa4,\\u5f20\\u534e\\u5e73,\\u4fde\\u9e3f\\u9b41,\\u7a0b\\u5b66\\u65d7. \\u57fa\\u4e8e\\u5c42\\u53e0\\u9690\\u9a6c\\u6a21\\u578b\\u7684\\u6c49\\u8bed\\u8bcd\\u6cd5\\u5206\\u6790; \\u8ba1\\u7b97\\u673a\\u7814\\u7a76\\u4e0e\\u53d1\\u5c55, 41\\u5377, No.8, 2004, pp.1421-pp.1429&nbsp; \\u88ab\\u5f15\\u752843\\u6b21<br>Hua-Ping ZHANG, Qun LIU, Hong-Kui YU, Xue-Qi CHENG, Shuo BAI. Chinese Name Entity Recognition Using Role Model.&nbsp; Special issue \\\"Word Formation and Chinese Language processing\\\" of the International Journal of Computational Linguistics and Chinese Language Processing, vol.8, No.2, 2003, pp. 29-602<br>\\u5f20\\u534e\\u5e73,\\u5218\\u7fa4.\\u57fa\\u4e8eN-\\u6700\\u77ed\\u8def\\u5f84\\u7684\\u4e2d\\u6587\\u8bcd\\u8bed\\u7c97\\u5206\\u6a21\\u578b. \\u4e2d\\u6587\\u4fe1\\u606f\\u5b66\\u62a5. 2002.9, Vol.16(5):pp.1-pp.7;&nbsp; \\u88ab\\u5f15\\u752834\\u6b21<\\\/p>\\n<h2><strong>\\u6559\\u5b66\\u60c5\\u51b5\\uff1a<\\\/strong><\\\/h2>\\n<p>\\u7814\\u7a76\\u751f\\u6559\\u5b66\\uff1a<a href=\\\"http:\\\/\\\/www.nlpir.org\\\/wordpress\\\/2018\\\/04\\\/01\\\/%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E4%B8%8E%E5%BA%94%E7%94%A8%E7%A1%95%E5%A3%AB%E7%8F%AD%E8%AF%BE%E7%A8%8B\\\/\\\">\\u5927\\u6570\\u636e\\u5206\\u6790\\u4e0e\\u5e94\\u7528<\\\/a><\\\/p>\\n<p>\\u672c\\u79d1\\u6559\\u5b66\\uff1a\\u5927\\u6570\\u636e\\u6280\\u672f&nbsp; 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Sheryta Yvette, \\u7855\\u58eb\\uff0c2018\\u7ea7\\uff0c2020\\u5e74\\u6bd5\\u4e1a\\u56de\\u56fd\\uff0c\\u8bba\\u6587\\uff08Ewe Language Processing using Transfer Learning\\uff09,\\u6765\\u81ea\\u591a\\u54e5<br>\\u4e07\\u91d1\\u6676 \\u5317\\u4eac\\u7406\\u5de5\\u5927\\u5b66 \\u7855\\u58eb 2017\\uff0c2020\\u5e74\\u6bd5\\u4e1a\\u5165\\u804c\\u822a\\u5929\\u8f6f\\u4ef6\\uff0c\\u6bd5\\u4e1a\\u8bba\\u6587\\uff08\\u57fa\\u4e8e\\u9886\\u57df\\u77e5\\u8bc6\\u7684\\u6587\\u672c\\u6821\\u5bf9\\u5173\\u952e\\u6280\\u672f\\u7814\\u7a76\\uff09<\\\/p>\\n<p>\\u6768\\u8000\\u98de \\u5317\\u4eac\\u4fe1\\u606f\\u79d1\\u6280\\u5927\\u5b66\\uff0c\\u5317\\u4eac\\u7406\\u5de5\\u5927\\u5b66\\u5ba2\\u5ea7\\u7855\\u58eb 2017\\uff0c2020\\u5e74\\u6bd5\\u4e1a\\uff0c\\u6bd5\\u4e1a\\u8bba\\u6587\\uff1a\\u795e\\u7ecf\\u673a\\u5668\\u7ffb\\u8bd1\\u7684\\u8bcd\\u7d20\\u7279\\u5f81\\u878d\\u5408<\\\/p>\\n<p>Iram Ishtiaq \\u5317\\u4eac\\u7406\\u5de5\\u5927\\u5b66 \\u7855\\u58eb 2016\\uff0c2018\\u5e74\\u6bd5\\u4e1a\\u5165\\u804c\\u4eba\\u5927\\u9644\\u4e2d\\uff08\\u5df4\\u57fa\\u65af\\u5766\\uff09\\uff0c\\u6bd5\\u4e1a\\u8bba\\u6587: <strong>Named Entity Recognition in Personality Profiling<\\\/strong><\\\/p>\\n<p>Ahmad Mafazi Damanhuri\\uff1a\\u5317\\u4eac\\u7406\\u5de5\\u5927\\u5b66 \\u7855\\u58eb 2016\\uff0c2018\\u5e74 \\u6bd5\\u4e1a\\u56de\\u56fd\\u4efb\\u6559(\\u5370\\u5c3c), \\u6bd5\\u4e1a\\u8bba\\u6587: PEOPLE PROFILING AND MODELING REPUTATION COMPUTATION BASED ON SENTIMENT ANALYSIS<\\\/p>\\n<p>\\u9ad8\\u8398&nbsp; 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Research on Uyghur morphological segmentation based on long sequence labeling method,5th International Conference on Signal Processing and Machine Learning (SPML 2022), Dalin China, Aug. 2022 (Accepted)<\\\/p>\\n<p>Asif Khan, Huaping Zhang*, Nada Boudjellal, Arshad Ahmad, Lin Dai, Jianyun Shang, Philipp Haindl, A Comparative Study Between Rule-Based and Transformer-Based Election Prediction Approaches: 2020 US Presidential Election as a Use Case, the 2nd International Workshop on AI System Engineering: Math, Modelling and Software (AISys2022), Vienna, Austria, Aug. 2022 (Accepted)<\\\/p>\\n<p>\\u5eb7\\u94e0,\\u5b8b\\u82e5\\u96e8,\\u675c\\u4f26,\\u5f20\\u534e\\u5e73,\\u90ed\\u5b87\\u822a\\uff0c\\u9762\\u5411\\u4e34\\u5e8a\\u53d1\\u73b0\\u7684\\u590d\\u6742\\u4fe1\\u606f\\u62bd\\u53d6,\\u4e2d\\u6587\\u4fe1\\u606f\\u5b66\\u62a5,2022(\\u5df2\\u5f55\\u7528)<\\\/p>\\n<p>\\u5f20\\u5b9d\\u534e\\uff0c\\u674e\\u6069\\u6797\\uff0c\\u5f20\\u534e\\u5e73\\uff0c\\u57fa\\u4e8e\\u5c42\\u6b21\\u7ed3\\u6784\\u7684\\u60c5\\u611f\\u5355\\u5143\\u8868\\u793a\\u65b9\\u6cd5\\uff0c\\u8ba1\\u7b97\\u673a\\u5de5\\u7a0b\\u4e0e\\u79d1\\u5b66.2021.&nbsp;<\\\/p>\\n<p>Linfang Wu, Hua-Ping Zhang, Yaofei Yang and Kai Gao;Dynamic Prototype Selection by Fusing Attention Mechanism for Few Shot Relation Classification;12th Asian Conference on Intelligent Information and Database Systems. Thailand;2020.3<br>Yaofei Yang, Hua-Ping Zhang, Linfang Wu and Yangsen Zhang;Cached Embedding with Random Selection: Optimization Technique to Improve Training Speed of Character-Aware Embedding;12th Asian Conference on Intelligent Information and Database Systems. Thailand;2020.3<\\\/p>\\n<p>Yang Y., Li S., Zhang Y., Zhang HP. (2019) Point the Point: Uyghur Morphological Segmentation Using PointerNetwork with GRU. In: Sun M., Huang X., Ji H., Liu Z., Liu Y. (eds) Chinese Computational Linguistics. CCL 2019. Lecture Notes in Computer Science, vol 11856. Springer, Cham \\uff08SCI Indexed\\uff09<\\\/p>\\n<p>\\\"Xi Zhang, Hua-Ping Zhang, Lei Zhao:<br>Reading More Efficiently: Multi-sentence Summarization with a Dual Attention and Copy-Generator Network. In: The Pacific Rim International Conferences on Artificial Intelligence, PRICAI 2018, Nanjing, Jiangsu, China, 28-31 August 2018, (1) 2018: 811-823\\\"<br>Shen Gao, Huaping Zhang, Kai Gao. A Convolutional Neural Network Based Sentiment Classification and the Convolutional Kernel Representation[C]. Proceedings of 22nd International Conference on Natural Language &amp; Information Systems(NLDB), Springer LNCS, Li\\u00e8ge, Belgium, 21-23 June, 2017, EI Compendex index<br>Shen Gao, Huaping Zhang, Kai Gao. Text Understanding with a Hybrid Neural Network Based Learning[C]. Proceedings of the 3th International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE), Springer LNCS, Changsha, China, 22-24 September, 2017, EI Compendex index.<br>SongZe Wu, Huaping Zhang, Chengcheng Xu, Tao Guo. Text Clustering on Short Message by Using Deep Semantic Representation. International Conference on Computational Sciences, Advanced Database and Computing, 2017.<br>\\\"CHENGCHENG XU, HUAPING ZHANG, BINGBING LU AND SONGZE WU. Local<br>Community Detection Using Social Relations and Topic Features in Social Networks<br>[M]. Chinese Computational Linguistics and Natural Language Processing Based on<br>Naturally Annotated Big Data. Springer, Cham, 2017, 371-383.\\\"<br>\\\"BINGBING LU, HUAPING ZHANG, BIN LIU AND ZHONGHUA ZHAO. Research on<br>User Identification Algorithm Based on Massive Multi-site VPN Log [C]. IEEE, International Conference on Communication Technology. IEEE, 2017:1372-1381\\\"<\\\/p>\\n<p>A Convolutional Neural Network Based Sentiment Classification and the Convolutional Kernel Representation<br>News Abridgement Algorithm Based on Word Alignment and Syntactic Parsing[M]\\\/\\\/ Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. 2016.<\\\/p>\\n<p>Qiao Y, Zhang Huaping(*), Yu M, et al. Sina-Weibo Spammer Detection with GBDT [M]\\\/\\\/ Social Media Processing. Springer Singapore, 2016.<br>Yang Qiao,Huaping Zhang*\\uff08\\u5f20\\u534e\\u5e73\\uff09,.Effective Detecting Microblog Spammers Using Big Data Fusion Algorithm.WorldComp'16,July 25-28,Las Vegas,Nevada,USA<\\\/p>\\n<p>Jiandong Zhou(\\u5468\\u5efa\\u680b\\uff09, Yanping Zhao\\uff08\\u8d75\\u71d5\\u5e73\\uff09, Huaping Zhang*\\uff08\\u5f20\\u534e\\u5e73\\uff09. Measuring Emotion Bifurcation Points for Individuals in Social Media[C].the Hawaii International Conference on System Science(\\u56fd\\u9645\\u7cfb\\u7edf\\u79d1\\u5b66\\u5e74\\u4f1a HICSS'49\\uff0c\\u7ba1\\u7406A\\u7c7b\\u4f1a\\u8bae, EI\\u68c0\\u7d22), Jan.5-8, 2016, Honolulu, KAUAI, Hawaii USA.2016:1949-1958.<\\\/p>\\n<p>\\u674e\\u7136,\\u5f20\\u534e\\u5e73,\\u8d75\\u71d5\\u5e73\\uff0c\\u5546\\u5efa\\u4e91.\\u57fa\\u4e8e\\u4e3b\\u9898\\u6a21\\u578b\\u4e0e\\u4fe1\\u606f\\u71b5\\u7684\\u4e2d\\u6587\\u6587\\u6863\\u81ea\\u52a8\\u6458\\u8981\\u6280\\u672f\\u7814\\u7a76(Automatic Text Summarization Research Based on Topic Model and Information Entropy).\\u8ba1\\u7b97\\u673a\\u79d1\\u5b66.2014-11<\\\/p>\\n<p>Kai Gao*, Hua-ping Zhang, Sheng-wang Li, Wei Wang, Jing Qiu;Research on Classification Algorithm and its Application in Cased-Based Reasoning,2014<\\\/p>\\n<p>\\u5f20\\u534e\\u5e73,\\u4ee5\\u4e3b\\u4f53\\u4e3a\\u4e2d\\u5fc3\\u7684\\u5fae\\u535a\\u8ba1\\u7b97\\u65b9\\u6cd5,\\u590d\\u6742\\u7cfb\\u7edf\\u4e0e\\u590d\\u6742\\u6027\\u79d1\\u5b66,2012\\u5e74\\u7b2c\\u56db\\u671f p84-91<\\\/p>\\n<p>Hua-Ping&nbsp;Zhang,Huan-Ping&nbsp;Wu,Jian&nbsp;Gao,Yan-Ping&nbsp;Zhao,Zhong-Liang&nbsp;Lv,Meteorological&nbsp;Bulletin&nbsp;Automatic&nbsp;Generation&nbsp;based&nbsp;on&nbsp;Spatio-Temporal&nbsp;Reasoning,In&nbsp;Proceedings&nbsp;of&nbsp;2011&nbsp;International&nbsp;Conference&nbsp;on&nbsp;Machine&nbsp;Learning&nbsp;and&nbsp;Cybernetics&nbsp;in&nbsp;Guangxi,&nbsp;China,2011.7\\uff0cp1927-1931<\\\/p>\\n<p>Hua-Ping Zhang, Qian Mo,He-Yang Huang,Structured POI data Extraction from Internet News,In Proceedings of the 4th International Universal Communication Symposium (IUCS 2010) in Beijing, China,2010.10\\uff0cp115-120(\\u7279\\u9080\\u62a5\\u544a)<br>Hua-Ping ZHANG,Jian GAO,Qian MO, He-Yan HUANG. Incorporating New Words Detection with Chinese Word Segmentation. In Proceedings of CIPS-SIGHAN Joint Conference on Chinese Language Processing (CLP 2010).Beijing, China.2010.8 .p249-251.<br>Hua-Ping ZHANG,Zhi-Hua LIU,Qian MO,He-Yan HUANG. Chinese Personal Name Disambiguation Based on Person Modeling. In Proceedings of CIPS-SIGHAN Joint Conference on Chinese Language Processing (CLP 2010).Beijing, China.2010.8 .p374-378<\\\/p>\\n<p>Hua-Ping Zhang, Hong-Bo Xu, Shuo Bai, Bin Wang, Xue-Qi Cheng. Experiments in TREC 2004 Novelty Track at CAS-ICT. In Proc. of the 13th Text Retrieval Conference, Gaithersburg, Maryland, November, 2004, pp287<\\\/p>\\n<p>Hua-Ping ZHANG, Qun LIU, Xue-Qi CHENG, Hao Zhang, Hong-Kui Yu. Chinese Lexical Analysis Using Hierarchical Hidden Markov Model, Second SIGHAN workshop affiliated with 41st ACL; Sapporo Japan, July, 2003, pp. 63-70<br>&nbsp;Hua-Ping ZHANG, Hong-Kui Yu, De-Yi Xiong, Qun LIU. HHMM-based Chinese Lexical Analyzer ICTCLAS, Second SIGHAN workshop affiliated with 41th ACL; Sapporo Japan, July, 2003, pp. 184-187<br>&nbsp;Kevin Zhang (Hua-Ping Zhang), Qun Liu, Hao Zhang, Xueqi Cheng. Automatic Recognition of Chinese Unknown Words Based on Role Tagging, First SIGHAN affiliated with 19th COLING, September 2002, pp71-77<\\\/p>\\n<\\\/div>\\n<p><!-- wp:paragraph --><\\\/p>\\n<p><!-- \\\/wp:paragraph --><!-- wp:paragraph --><\\\/p>\\n<p><!-- \\\/wp:paragraph --><\\\/p>\",\"eael_vto_writing_gradient_color_repeater\":[{\"eael_vto_writing_gradient_color\":\"#7C62FF\",\"eael_vto_writing_gradient_color_location\":{\"unit\":\"%\",\"size\":50},\"_id\":\"1571651\"},{\"eael_vto_writing_gradient_color\":\"#FF6464\",\"eael_vto_writing_gradient_color_location\":{\"unit\":\"%\",\"size\":90},\"_id\":\"185ad27\"}]},\"elements\":[],\"widgetType\":\"text-editor\"}],\"isInner\":false}]","_elementor_page_settings":{"eael_ext_toc_title":"Table of Contents"}},"categories":[33,35,36],"tags":[],"class_list":["post-48","post","type-post","status-publish","format-standard","hentry","category-experts","category-dr-kevinzhang-","category-staff"],"_links":{"self":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts\/48","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/comments?post=48"}],"version-history":[{"count":169,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts\/48\/revisions"}],"predecessor-version":[{"id":3382669,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/posts\/48\/revisions\/3382669"}],"wp:attachment":[{"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/media?parent=48"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/categories?post=48"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.nlpir.org\/wordpress\/wp-json\/wp\/v2\/tags?post=48"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}