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Title: A semantic frame-based intelligent agent for topic detection
Authors: Chang, Yung-Chun;Hsieh, Yu-Lun;Chen, Cen-Chieh;Hsu, Wen-Lian
Contributors: 資訊科學系
Keywords: Topic detection;Semantic frame;Semantic class;Partial matching
Date: 2017-01
Issue Date: 2015-08-27 17:17:22 (UTC+8)
Abstract: Detecting the topic of documents can help readers construct the background of the topic and facilitate document comprehension. In this paper, we propose a semantic frame-based topic detection (SFTD) that simulates such process in human perception. We take advantage of multiple knowledge sources and extracted discriminative patterns from documents through a highly automated, knowledge-supported frame generation and matching mechanisms. Using a Chinese news corpus containing over 111,000 news articles, we provide a comprehensive performance evaluation which demonstrates that our novel approach can effectively detect the topic of a document by exploiting the syntactic structures, semantic association, and the context within the text. Experimental results show that SFTD is comparable to other well-known topic detection methods.
Relation: Soft Computing, Volume 21, Issue 2, pp 391–401
Data Type: article
DOI 連結:
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