NLPIR/ICTCLA2018 ACADEMIC SEMINAR 2st ISSUE

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NLPIR/ICTCLA2018 ACADEMIC SEMINAR 2st ISSUE自然语言处理与信息检索共享平台G6j~!F9uW S(DQA

INTRO

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In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Wednesdays, and each time a keynote speaker will share understanding of papers published in recent years with you.

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Arrangement自然语言处理与信息检索共享平台DNM1WR$S G

This week's seminar is organized as follows:自然语言处理与信息检索共享平台[{(kE0_;mp-S2J

1. The seminar time is 1.pm, Wed, at Zhongguancun Technology Park ,Building 5, 1036.自然语言处理与信息检索共享平台 @|;X1aD

2. The lecturer is Nihad, the paper's title is Growing Wikipedia Across Languages via Recommendation.

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3. The seminar will be hosted Gang Wang.自然语言处理与信息检索共享平台^T9zg;y oV3uR1F

4. Attachment is the paper of this seminar, please download in advance自然语言处理与信息检索共享平台$Xgo4|_zo }W

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

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Growing Wikipedia Across Languages via Recommendation自然语言处理与信息检索共享平台Dh t!["DL6f[ c:^

Ellery Wulczyn             Robert West         Leila Zia              Jure Leskovec

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Abstract自然语言处理与信息检索共享平台f"F"~:P A)AU

The differentWikipedia language editions vary dramatically in how comprehensive they are. As a result, most language editions contain only a small fraction of the sum of information that exists across all Wikipedias. In this paper, we present an approach to filling gaps in article coverage across different Wikipedia editions. Our main contribution is an end-to-end system for recommending articles for creation that exist in one language but are missing in another. The system involves identifying missing articles, ranking the missing articles according to their importance, and recommending important missing articles to editors based on their interests. We empirically validate our models in a controlled experiment involving 12,000 French Wikipedia editors. We find that personalizing recommendations increases editor engagement by a factor of two. Moreover, recommending articles increases their chance of being created by a factor of 3.2. Finally, articles created as a result of our recommendations are of comparable quality to organically created articles. Overall, our system leads to more engaged editors and faster growth of Wikipedia with no effect on its quality.自然语言处理与信息检索共享平台VM9Dgo/H%ZZ

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