潜在语义分析的供求信息自动匹配算法

doi:10.3969/j.issn.1001-2400.2012.03.020

Abstract

Abstract:

In traditional Supply and Demand Information Matching models, a word is regarded as an independent unit. However, there are many synonyms and polysemy in the natural language and their existence has deteriorated the precision. In order to solve this problem, Latent Semantic Analysis is applied to it. Moreover, an algorithm based on Entropy is proposed to improve the weighting of Latent Semantic Analysis. A Supply and Demand Information Automatic Matching algorithm based on Latent Semantic Analysis, Rule Extraction and Relevance Feedback is realized. And a Supply and Demand Information Base is designed to support it. Experimental results show that the precision of this algorithm is much better than that of the method based on the Vector Space Model.

Key words: latent semantic analysis, entropy, semantic, supply and demand information matching, vector space model

FENG Yuejin;ZHANG Fengbin. Automatic matching algorithm for the latent semantic analysis based supply and demand information[J].J4, 2012, 39(3): 126-130.

References

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Automatic matching algorithm for the latent semantic analysis based supply and demand information

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