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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111981

Title:	Semantic frame-based statistical approach for topic detection
Authors:	謝宇倫 Chang, Y.-C. Hsieh, Yu Lun Chen, Cenchieh Liu, C. Lu, C.-H. Hsu, W.-L.
Contributors:	資管系
Date:	2014
Issue Date:	2017-08-16 16:56:14 (UTC+8)
Abstract:	We propose a statistical frame-based approach (FBA) for natural language processing, and demonstrate its advantage over traditional machine learning methods by using topic detection as a case study. FBA perceives and identifies semantic knowledge in a more general manner by collecting important linguistic patterns within documents through a unique flexible matching scheme that allows word insertion, deletion and substitution (IDS) to capture linguistic structures within the text. In addition, FBA can also overcome major issues of the rule-based approach by reducing human effort through its highly automated pattern generation and summarization. Using Yahoo! Chinese news corpus containing about 140,000 news articles, we provide a comprehensive performance evaluation that demonstrates the effectiveness of FBA in detecting the topic of a document by exploiting the semantic association and the context within the text. Moreover, it outperforms common topic models like Näive Bayes, Vector Space Model, and LDA-SVM. Copyright 2014 by Yung-Chun Chang, Yu-Lun Hsieh, Cen-Chieh Chen.
Relation:	Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014,75-84 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014; Cape Panwa HotelPhuket; Thailand; 12 December 2014 到 14 December 2014; 代碼 124040
Data Type:	conference
Appears in Collections:	[Department of MIS] Proceedings

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