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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
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| 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: | [資訊管理學系] 會議論文
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Files in This Item:
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| Y14-1011.pdf | | 2078Kb | Adobe PDF2 | 377 | View/Open |
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