Abstract/Details

Topic models in information retrieval


2007 2007

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Abstract (summary)

Topic modeling demonstrates the semantic relations among words, which should be helpful for information retrieval tasks. We present probability mixture modeling and term modeling methods to integrate topic models into language modeling framework for information retrieval. A variety of topic modeling techniques, including manually-built query models, term similarity measures and latent mixture models, especially Latent Dirichlet Allocation (LDA), a formal generative latent mixture model of documents, have been proposed or introduced into IR tasks. We investigated and evaluated them on several TREC collections within presented frameworks, and show that significant improvements over previous work can be obtained. Practical problems such as efficiency and scaling considerations are discussed and compared for different topic models. Other recent topic modeling techniques are also discussed.

Indexing (details)


Subject
Computer science
Classification
0984: Computer science
Identifier / keyword
Applied sciences; Information retrieval; Topic modeling
Title
Topic models in information retrieval
Author
Wei, Xing
Number of pages
145
Publication year
2007
Degree date
2007
School code
0118
Source
DAI-B 68/11, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549330257
Advisor
Croft, W. Bruce
Committee member
Allan, James; McCallum, Andrew K.; Staudenmayer, John
University/institution
University of Massachusetts Amherst
Department
Computer Science
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3289216
ProQuest document ID
304847443
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304847443
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