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Communication Dans Un Congrès Année : 2002

Contribution to Topic Identification by Using Word Similarity

Résumé

In this paper, a new topic identification method, WSIM, is investigated. It exploits the similarity between words and topics. This measure is a function of the similarity between words, based on the mutual information. The performance of WSIM is compared to the cache model and to the well-known SVM classifier. Their behavior is also studied in terms of recall and precision, according to the training size. Performance of WSIM reaches 82.4 % correct topic identification. It outperforms SVM (76.2%) and has a comparable performance with the cache model (82.0\%).
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Dates et versions

inria-00100947 , version 1 (26-09-2006)

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  • HAL Id : inria-00100947 , version 1

Citer

Armelle Brun, Kamel Smaïli, Jean-Paul Haton. Contribution to Topic Identification by Using Word Similarity. 7th International Conference on Spoken Language Processing - ICSLP'2002, Sep 2002, Denver, Colorado, USA, 4 p. ⟨inria-00100947⟩
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