Abstract: How to improve the rankings of the relevant documents plays a key role in information retrieval. In this paper, a re-ranking approach based on topic words pair is proposed to improve precision while recall is preserved. The topic word pairs contain two correlated words, one of which is the original query word and the other come from the documents. The selection is based on Probabilistic Latent Semantic Indexing (PLSI). Then, the distribution of the word pairs is used to re-rank documents. Results show a 53.6% and 55.8% improvement compare to the initial retrieval without any re-ranking or query expansion on NTCIR-5 document collection for SLIR.

摘要： 信息检索中相关文档的排序一直是一个至关重要的问题。本文提出一种基于主题词对的文档重排方法，使得检索结果在保持召回率的前提下提高精确率。主题词对意指能够共同表征同一主题的两个词语，其中一个来自于查询，另一个来自于文档，两者之间具有紧密的联系。本文中，主题词对的选择采用概率潜在语义索引的方法，并根据主题词对在文档中的分布状况对其进行重排。对NTCIR-5中文信息检索的文档集合进行测试，采用trec标准评估方法，结果表明采用该方法使得精确率在rigid和relax结果集上分别提高了53.6% 和55.8%。