Learning question focus and semantically related features from Web search results for Chinese question classification

Shu Jung Lin, Wen Hsiang Lu

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

Recently, some machine learning techniques like support vector machines are employed for question classification. However, these techniques heavily depend on the availability of large amounts of training data, and may suffer many difficulties while facing various new questions from the real users on the Web. To mitigate the problem of lacking sufficient training data, in this paper, we present a simple learning method that explores Web search results to collect more training data automatically by a few seed terms (question answers). In addition, we propose a novel semantically related feature model (SRFM), which takes advantage of question focuses and their semantically related features learned from the larger number of collected training data to support the determination of question type. Our experimental results show that the proposed new learning method can obtain better classification performance than the bigram language modeling (LM) approach for the questions with untrained question focuses.

原文English
主出版物標題Information Retrieval Technology - Third Asia Information Retrieval Symposium, AIRS 2006, Proceedings
發行者Springer Verlag
頁面284-296
頁數13
ISBN(列印)3540457801, 9783540457800
DOIs
出版狀態Published - 2006
事件3rd Asia Information Retrieval Symposium, AIRS 2006 - Singapore, Singapore
持續時間: 2006 10月 162006 10月 18

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4182 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other3rd Asia Information Retrieval Symposium, AIRS 2006
國家/地區Singapore
城市Singapore
期間06-10-1606-10-18

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 一般電腦科學

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