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Latent Representations for Information Retrieval

模式识别学术大讲堂
Advanced Lecture Series in Pattern Recognition
题    目 (TITLE):Latent Representations for Information Retrieval
讲 座 人 (SPEAKER):Prof. Jian-Yun Nie (University of Montreal)
主 持 人 (CHAIR): Prof. Chengqing Zong
时    间 (TIME):June 1 (Wednesday), 2016, 10:00 AM
地    点 (VENUE):No.3 Conference Room (3rd floor), Intelligence Building
报告摘要(ABSTRACT):
Traditional information retrieval uses words as the basic representation units. It is known that such a representation has several problems, in particular, when dealing with synonymous and polysemous words. These problems are particularly important for information retrieval. A series of latent representations have been used to address the problems, ranging from LSA, LDA to more recent embeddings. In this talk, we will review these representations for IR applications. It will be shown that latent representations can help solve the problems to some extent, but cannot (yet) fully replace the traditional word-based representation. We will provide some analysis on this.
报告人简介(BIOGRAPHY):
Jian-Yun Nie is a professor in University of Montreal. He has been working in the areas of information retrieval and natural language processing for a long time. His research topics include information retrieval models, cross-language information retrieval, query expansion and understanding, etc. Jian-Yun Nie has published a number of papers on these topics in the top journals and conferences. His papers have been widely cited by peers. He is on the editorial board of 7 international journals, and is a regular PC member of the major conferences in these areas such as SIGIR, CIKM, ACL. He was also the general chair of SIGIR 2011.

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中科院自动化研究所 模式识别国家重点实验室
NLPR, INSTITUTE OF AUTOMATION, CHNESE ACADEMY OF SCIENCES