NDLI: Dirichlet Process Mixture Models based topic identification for short text streams

Content Provider	IEEE Xplore Digital Library
Author	Chan Wang Caixia Yuan Xiaojie Wang Wenwei Xue
Copyright Year	2011
Description	Author affiliation: Nokia Research Growth Economies Lab, Beijing, China (Wenwei Xue) \|\| Center of Intelligent Science and Technology, Beijing University of Posts and Telecommunications, China (Chan Wang; Caixia Yuan; Xiaojie Wang)
Abstract	Topic detection and tracking (TDT) has been extensively studied and applied in recent years. However, prior work is mostly based on regular news text, the problem of scaling to short stories remains pretty much open. Besides, prior work conducts topic identification on separated stories by assuming story segmentation as prerequisites, which is another challenging yet critical task for TDT research. In this paper, we propose a Dirichlet Process Mixture Model (DPMM) based topic identification method, which deals with topic segmentation, topic detection and tracking in an unified model, and achieves reasonable results for short stories. We first present DPMM and its application in topic identification task. Then we discuss two different solutions specifically designed to solve sparseness problem associated with short stories. One is the design of algorithm flow. Instead of a single short text, the processing unit of topic identification is converted to session firstly. The other applies extended DPMM model which takes account of word dependency when estimating distributions of words associated with every known topic. Whereafter, we extend DPMM to identify topic for spontaneous text streams by managing topic segmentation, topic detection and tracking simultaneously. The attractive advantage of DPMM is the number of mixture components needs not been fixed in advance, and it does not need prior knowledge about number and content of topics. So compared with other existing methods, it is more suitable for streaming topic identification. Our empirical results on TDT3 evaluation data verify that DPMM is valid in the task of topic identification for short text data with stream properties, and extended DPMM outperforms original DPMM methods.
Starting Page	80
Ending Page	87
File Size	749653
Page Count	8
File Format	PDF
ISBN	9781612847290
e-ISBN	9781612847290
DOI	10.1109/NLPKE.2011.6138173
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-27
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dirichlet Process Mixture Model data streams topic identification extended DPMM DPMM static short text Manganese
Content Type	Text
Resource Type	Article

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	Mr. Siddhartha Mukherjee siddhartha@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	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
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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