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  • 标题:Question Retrieval Based on Probabilistic Latent Semantic Analysis in Q & A Community
  • 本地全文:下载
  • 作者:Chengfang Tan ; Hong Li ; Yundong Liu
  • 期刊名称:The Open Cybernetics & Systemics Journal
  • 电子版ISSN:1874-110X
  • 出版年度:2014
  • 卷号:8
  • 期号:1
  • 页码:1033-1037
  • DOI:10.2174/1874110X01408011033
  • 出版社:Bentham Science Publishers Ltd
  • 摘要:

    With the increasingly popularity of Q & A Community, it has become an important means for people to retrieve question from question library to find the answer. Similarity calculation is the core issue in Q & A community, and the appropriate calculation method is the key factor that affects the quality of question retrieval. This paper proposes a retrieval method based on PLSA model. Firstly, we modelled the question library, and got the probability distribution of "question document –latent semantic -word". Secondly, we calculated the semantic similarity between questions and classify them. Finally, based on user retrieval content, we calculated the similarity between question documents and query, then the query results will be returned to the user in descending order according to the value. Compared with other similarity calculation methods that use VSM, HNS and SD, the experimental results show that this proposed method has a high precision rate.

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