NDLI: Exploiting semantic associative information in topic modeling

Content Provider	IEEE Xplore Digital Library
Author	Meng-Sung Wu Hung-Shin Lee Hsin-Min Wang
Copyright Year	2010
Description	Author affiliation: Institute of Information Science, Academia Sinica, Taipei, Taiwan (Meng-Sung Wu; Hung-Shin Lee; Hsin-Min Wang)
Abstract	Topic modeling has been widely applied in a variety of text modeling tasks as well as in speech recognition systems for effectively capturing the semantic and statistic information in documents or speech utterances. Most topic models rely on the bag-of-words assumption that results in learned latent topics composed of lists of individual words. Unfortunately, these words may convey topical information but lack accurate semantic knowledge of the text. In this paper, we present the semantic associative topic model, where the concept of the semantic association terms is extended to topic modeling, which provides guidance on modeling the semantic associations that occur among single words by expressing a document as an association of multiple words. Further, the pointwise KL-divergence metric is used to measure the significance of the association. We also integrate original PLSA and SATM models, which have mixed feature representations. Experimental results on WSJ and AP datasets show that the proposed approaches achieved higher performance compared to other methods.
Starting Page	384
Ending Page	388
File Size	219307
Page Count	5
File Format	PDF
ISBN	9781424479047
e-ISBN	9781424479023
DOI	10.1109/SLT.2010.5700883
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Biological system modeling topic model Semantics information retrieval semantic association Information retrieval Probabilistic logic language model Mathematical model Matrix decomposition
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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