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https://ah.lib.nccu.edu.tw/handle/140.119/111981
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 | 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 |
Appears in Collections: | 會議論文 |
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File | Description | Size | Format | |
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Y14-1011.pdf | 2.08 MB | Adobe PDF2 | View/Open |
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