NDLI: Modeling Both Coarse-Grained and Fine-Grained Topics in Massive Text Data

Content Provider	IEEE Xplore Digital Library
Author	Weifan Zhang Hui Zhang Yuan Zuo Deqing Wang
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci., Beihang Univ., Beijing, China (Weifan Zhang) \|\| Nat. Eng. Res. Center of S&T Resources Sharing Service, Beihang Univ., Beijing, China (Hui Zhang; Yuan Zuo) \|\| Sch. of Econ. & Manage., Beihang Univ., Beijing, China (Deqing Wang)
Abstract	Topic model has attracted much attention from investigators, as it provides users with insights into the huge volumes of documents. However, most previous related studies that based on Non-negative Matrix Factorization (NMF) neglect to figure out which topics are widespread in the documents and which are not. These widespread topics, which we refer to coarse-grained topics, have great significance for people who concentrate on common topics in a given text set. For example, after reading the massive job ads, the jobseekers are eager to learn employers' basic requirements which can be regarded as the coarse-grained topics, as well as the additional requirements that can be deemed to be the fine-grained topics. In this paper, we propose a novel method which applies two different sparseness constraints to NMF to tell coarse-grained topics and fine-grained topics apart. The experimental results of demonstrate that the new model can not only discover coarse-grained topics but also extract fine-grained topics. We evaluate the performance of the new model via text clustering and classification, and the results show the new model can learn more accurate topic representations of documents.
Starting Page	378
Ending Page	383
File Size	600551
Page Count	6
File Format	PDF
ISBN	9781479981281
DOI	10.1109/BigDataService.2015.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Text mining Electronic publishing Non-negative matrix factorization Encyclopedias Text clustering Internet Matrix decomposition Optimization Topic model
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
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
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