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Contribution to Topic Identification by Using Word Similarity

Armelle Brun 1 Kamel Smaïli 1 Jean-Paul Haton 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : 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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Submitted on : Tuesday, September 26, 2006 - 2:53:04 PM
Last modification on : Friday, February 26, 2021 - 3:28:05 PM


  • HAL Id : inria-00100947, version 1



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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