期刊论文、会议论文

Huang, Jimmy Xiangji

英文

SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL

2016

733-736

39th International ACM SIGIR conference on Research and Development in Information Retrieval

JUL 17-21, 2016

Pisa, ITALY

[Huang, Jimmy Xiangji] Cent China Normal Univ, Informat Retrieval & Knowledge Management Res Lab, Wuhan, Hubei, Peoples R China.^[Jian, Fanghong] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.^[He, Tingting;Hu, Po] Cent China Normal Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.^[Zhao, Jiashu] York Univ, Sch Informat Technol, Toronto, ON, Canada.

ACM SIGIR

1515 BROADWAY, NEW YORK, NY 10036-9998 USA

ASSOC COMPUTING MACHINERY

978-1-4503-4069-4

10.1145/2911451.2914748

Natural Sciences & Engineering Research Council (NSERC) of CanadaNatural Sciences and Engineering Research Council of Canada (NSERC); NSERC CREATE award; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC)

本校为第一且通讯机构

Traditional information retrieval (IR) models, in which a document is normally represented as a bag of words and their frequencies, capture the term-level and document-level information. Topic models, on the other hand, discover semantic topic-based information among words. In this paper, we consider term-based information and semantic information as two features of query terms and propose a simple enhancement for ad-hoc IR via topic modeling. In particular, three topic-based hybrid models, LDA-BM25, LDA-MATF and LDA-LM, are proposed. A series of experiments on eight standard datasets show tha...