PKDE4J: Entity and relation extraction for public knowledge discovery – NLPIR自然语言处理与信息检索共享平台

自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

PKDE4J: Entity and relation extraction for public knowledge discovery

NLPIR SEMINAR Y2019#16

INTRO

In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.

Arrangement

This week’s seminar is organized as follows:

  1. The seminar time is 1.pm, Mon, at Zhongguancun Technology Park ,Building 5, 1306.
  2. The lecturer is Nada, the paper’s title is PKDE4J: Entity and relation extraction for public knowledge discovery.
  3. ShenLi will give the presentation of her work.
  4. The seminar will be hosted by Baohua Zhang.
  5. Attachment is the paper of this seminar, please download in advance.

Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.

PKDE4J: Entity and relation extraction for public knowledge discovery

Min Song, Won Chul Kim, Dahee Lee, Go Eun Heo, Keun Young Kang

Abstract

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction.

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