NDLI: Distributional Similarity Model for Multi-modality Clustering in Social Media

Content Provider	IEEE Xplore Digital Library
Author	Sze, D.C.M. Tak-chung Fu Fu-lai Chung Luk, R.W.P.
Copyright Year	2007
Description	Author affiliation: Hong Kong Polytech. Univ., Hong Kong (Sze, D.C.M.; Tak-chung Fu; Fu-lai Chung; Luk, R.W.P.)
Abstract	User generated content (UGC) has become the fastest growing sector of the WWW. Data mining from UGC presents challenges not typically found in text mining from documents. UGC can be semi-structured and its content can be very short and informal, containing relatively little content similar to a chat or an email conversation. In addition UGC can be viewed as a multi-modality data. These characteristics pose big challenges and research questions for scholars to cope with. To cluster UGC data, we can construct multiple contingency tables of modalities and employ the multi-way distributional clustering (MDC) algorithm. However, by considering a contingency table which summarizes the co-occurrence statistics of two modalities, it is not robust to represent the information entropy between two modalities in UGC data. In this paper, we propose a novel similarity measurement, called distributional similarity model (DSM), to solidify the graph model in the MDC algorithm to deal with the unique characteristics of the UGC data.
Starting Page	268
Ending Page	271
File Size	495828
Page Count	4
File Format	PDF
ISBN	0769530281
DOI	10.1109/WI-IATW.2007.105
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text mining Solid modeling Machine learning algorithms User-generated content Clustering algorithms Social Media AnalysisMulti-Modality ClusteringDistributional Features World Wide Web Robustness Data mining Information entropy Intelligent agent
Content Type	Text
Resource Type	Article

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