Semantic frame-based statistical approach for topic detection

Yung Chun Chang, Yu Lun Hsieh, Cen Chieh Chen, Chad Liu, Chun Hung Lu, Wen Lian Hsu

研究成果: 書貢獻/報告類型會議貢獻

3 引文 斯高帕斯(Scopus)

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.
原文英語
主出版物標題Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014
發行者Faculty of Pharmaceutical Sciences, Chulalongkorn University
頁面75-84
頁數10
ISBN(電子)9786165518871
出版狀態已發佈 - 2014
對外發佈
事件28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014 - Phuket, 泰国
持續時間: 十二月 12 2014十二月 14 2014

會議

會議28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014
國家/地區泰国
城市Phuket
期間12/12/1412/14/14

ASJC Scopus subject areas

  • 語言與語言學
  • 電腦科學(雜項)

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