NDLI: Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior

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Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior

Content Provider	ACM Digital Library
Author	Nakagawa, Hiroshi Sato, Issei
Abstract	Documents, such as those seen on Wikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data.Therefore, it is more and more important to analyze a relationship between a document and topics assigned to the document. In this paper, we proposed a novel probabilistic generative model of documents with multiple topics as a meta-data. By focusing on modeling the generation process of a document with multiple topics, we can extract specific properties of documents with multiple topics.Proposed model is an expansion of an existing probabilistic generative model: Parametric Mixture Model (PMM). PMM models documents with multiple topics by mixing model parameters of each single topic. Since, however, PMM assigns the same mixture ratio to each single topic, PMM cannot take into account the bias of each topic within a document. To deal with this problem, we propose a model that considers Dirichlet distribution as a prior distribution of the mixture ratio.We adopt Variational Bayes Method to infer the bias of each topic within a document. We evaluate the proposed model and PMM using MEDLINE corpus.The results of F-measure, Precision and Recall show that the proposed model is more effective than PMM on multiple-topic classification. Moreover, we indicate the potential of the proposed model that extracts topics and document-specific keywords using information about the assigned topics.
Starting Page	590
Ending Page	598
Page Count	9
File Format	PDF
ISBN	9781595936097
DOI	10.1145/1281192.1281256
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Probability model Text clustering Dirichlet distribution Multiple topic Variational bayes method
Content Type	Text
Resource Type	Article

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