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題名 Semantic frame-based statistical approach for topic detection
作者 謝宇倫
Chang, Y.-C.
Hsieh, Yu Lun
Chen, Cenchieh
Liu, C.
Lu, C.-H.
Hsu, W.-L.
貢獻者 資管系
日期 2014
上傳時間 16-Aug-2017 16:56:14 (UTC+8)
摘要 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.
關聯 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
資料類型 conference
dc.contributor 資管系zh_Tw
dc.creator (作者) 謝宇倫zh_TW
dc.creator (作者) Chang, Y.-C.en_US
dc.creator (作者) Hsieh, Yu Lunen_US
dc.creator (作者) Chen, Cenchiehen_US
dc.creator (作者) Liu, C.en_US
dc.creator (作者) Lu, C.-H.en_US
dc.creator (作者) Hsu, W.-L.en_US
dc.date (日期) 2014en_US
dc.date.accessioned 16-Aug-2017 16:56:14 (UTC+8)-
dc.date.available 16-Aug-2017 16:56:14 (UTC+8)-
dc.date.issued (上傳時間) 16-Aug-2017 16:56:14 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/111981-
dc.description.abstract (摘要) 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.en_US
dc.format.extent 2128241 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014,75-84en_US
dc.relation (關聯) 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014; Cape Panwa HotelPhuket; Thailand; 12 December 2014 到 14 December 2014; 代碼 124040zh_TW
dc.title (題名) Semantic frame-based statistical approach for topic detectionen_US
dc.type (資料類型) conference