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2006
Springer

Word Sense Language Model for Information Retrieval

10 years 2 months ago
Word Sense Language Model for Information Retrieval
This paper proposes a word sense language model based method for information retrieval. This method, differing from most of traditional ones, combines word senses defined in a thesaurus with a classic statistical model. The word sense language model regards the word sense as a form of linguistic knowledge, which is helpful in handling mismatch caused by synonym and data sparseness due to data limit. Experimental results based on TREC-Mandarin corpus show that this method gains 12.5% improvement on MAP over traditional tf-idf retrieval method but 5.82% decrease on MAP compared to a classic language model. A combination result of this method and the language model yields 8.92% and 7.93% increases over either respectively. We present analysis and discussions on the not-so-exciting results and conclude that a higher performance of word sense language model will owe to high accurate of word sense labeling. We believe that linguistic knowledge such as word sense of a thesaurus will help IR i...
Liqi Gao, Yu Zhang, Ting Liu, Guiping Liu
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where AIRS
Authors Liqi Gao, Yu Zhang, Ting Liu, Guiping Liu
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