摘要
Multi-source information can be utilized collaboratively to improve the performance of information retrieval. To make full use of the document and collection information, this paper introduces a new informa- tion retrieval model that relies on the Dempster-Shafer theory of evidence. Each query-document pair is taken as a piece of evidence for the relevance between a document and a query. The evidence is combined using Dempster＇s rule of combination, and the belief committed to the relevance is obtained. Retrieved documents are then ranked according to the belief committed to the relevance. Several basic probability as- signments are also proposed. Extensive experiments over the Text REtrieval Conference （TREC） test col- lection ClueWeb09 show that the proposed model provides performance similar to that of the Vector Space Model （VSM）. Under certain probability assignments, the proposed model outperforms the VSM by 63% in terms of mean average precision,
Multi-source information can be utilized collaboratively to improve the performance of information retrieval. To make full use of the document and collection information, this paper introduces a new informa- tion retrieval model that relies on the Dempster-Shafer theory of evidence. Each query-document pair is taken as a piece of evidence for the relevance between a document and a query. The evidence is combined using Dempster＇s rule of combination, and the belief committed to the relevance is obtained. Retrieved documents are then ranked according to the belief committed to the relevance. Several basic probability as- signments are also proposed. Extensive experiments over the Text REtrieval Conference （TREC） test col- lection ClueWeb09 show that the proposed model provides performance similar to that of the Vector Space Model （VSM）. Under certain probability assignments, the proposed model outperforms the VSM by 63% in terms of mean average precision,
基金
Supported by the Self-Directed Program of Tsinghua University (No. 2011Z01033)
