NDLI: Probabilistic Word Selection via Topic Modeling

Content Provider	IEEE Xplore Digital Library
Author	Yueting Zhuang Haidong Gao Fei Wu Siliang Tang Yin Zhang Zhongfei Zhang
Copyright Year	1989
Abstract	We propose selective supervised Latent Dirichlet Allocation (ssLDA) to boost the prediction performance of the widely studied supervised probabilistic topic models. We introduce a Bernoulli distribution for each word in one given document to selectthis word as a strongly or weakly discriminative one with respect to its assigned topic. The Bernoulli distribution is parameterized by the discrimination power of the word for its assigned topic. As a result, the document is represented as a “bag-of-selective-words” instead of the probabilistic “bag-of-topics” in the topic modeling domain or the flat “bag-of-words” in the traditional natural language processing domain to form a new perspective. Inheriting the general framework of supervised LDA (sLDA), ssLDA can also predict many types of response specified by a Gaussian Linear Model (GLM). Focusing on the utilization of this word selection mechanism for singe-label document classification in this paper, we conduct the variational inference for approximating the intractable posterior and derive a maximum-likelihood estimation of parameters in ssLDA. The experiments reported on textual documents show that ssLDA not only performs competitively over “state-of-the-art” classification approaches based on both the flat “bag-of-words” and probabilistic “bag-of-topics” representation in terms of classification performance, but also has the ability to discover the discrimination power of the words specified in the topics (compatible with our rational knowledge).
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	1643
Ending Page	1655
Page Count	13
File Size	1298695
File Format	PDF
ISSN	10414347
Volume Number	27
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probabilistic logic Data models Predictive models Resource management Convergence Analytical models Equations Classification Topic modeling Latent Dirichlet Allocation Supervised learning classification latent Dirichlet allocation supervised learning
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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