Improved Chinese spoken document retrieval with hybrid modeling and data-driven indexing features

Chun Jen Wang, Berlin Chen, Lin Shan Lee

研究成果: 會議貢獻類型會議論文同行評審

5 引文 斯高帕斯(Scopus)

摘要

Different models retrieve the documents based on different approaches of extracting the underlying content. Different levels of indexing features also offer different functionalities and discriminabilities when retrieving the documents. In this paper, we present results for Chinese spoken document retrieval with hybrid models to integrate the knowledge obtainable from three basic retrieval models, namely, the standard vector space model (VSM), the hidden Markov model (HMM), and the latent semantic indexing (LSI) model. The characteristics of retrieval performance using both word-level and syllable-level indexing features were extensively explored. In addition, a data-driven approach to derive variable-length indexing features is also presented. Very satisfactory performance can be achieved with these data-driven features while retaining very compact feature set size. Experiments showed that this approach has the potential to identify domain-specific terminologies or newlygenerated phrases. It is therefore very useful not only in Chinese document retrieval, but also in detecting out of vocabulary (OOV) words in Chinese. Very encouraging results were obtained when the hybrid models were used with the datadriven indexing features as well.

原文英語
頁面1985-1988
頁數4
出版狀態已發佈 - 2002
對外發佈
事件7th International Conference on Spoken Language Processing, ICSLP 2002 - Denver, 美国
持續時間: 2002 9月 162002 9月 20

其他

其他7th International Conference on Spoken Language Processing, ICSLP 2002
國家/地區美国
城市Denver
期間2002/09/162002/09/20

ASJC Scopus subject areas

  • 語言與語言學
  • 語言和語言學

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