[1]ZHOU Yipeng,DU Junping.Semantic tagging of a topic model based on associated words[J].CAAI Transactions on Intelligent Systems,2012,7(4):327-332.
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Semantic tagging of a topic model based on associated words

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 4 Page number: 327-332 Column: 学术论文—自然语言处理与理解 Public date: 2012-08-25
Title:
Semantic tagging of a topic model based on associated words
Author(s):
ZHOU Yipeng1;  DU Junping2
1. School of Computer Science and Information Engineering, Beijing Technology and Business University, Beijing 100048, China; 
2. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China
Keywords:
topic analysis;  semantic tagging;  generative model;  associated words;  association rule
CLC:
TP391
DOI:
-
Abstract:
In topic analysis field of Internet, the probabilistic topic model is often used to describe topic semanteme. But the semanteme of a topic model is difficult for users to understand. An automatic semantic tagging method of a probabilistic topic model is proposed. Firstly, an association rule mining algorithm based on semantic categories is presented to get associated topic words, which consist of a candidate tag set. Then, according to the probability of associated words, a semantic correlation function is used to calculate semantic correlation of candidate tags and topic model. At last, a maximal marginal relevance method is used to select tags with better semantic coverage and discrimination. The experimental results of food safety and tourism topic model proved that, compared with maximum probability topic words tagging method, the proposed method can improve accuracy of topic tagging obviously, and can express more abundant semantemes with a small number of tags, which solve the problem of single semantic category in the multitagging method. So it is helpful to achieve more accurate topic tracking and topic information retrieval.
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