Improving accuracy for identifying related PubMed queries by an integrated approach.

ABSTRACT: PubMed is the most widely used tool for searching biomedical literature online. As with many other online search tools, a user often types a series of multiple related queries before retrieving satisfactory results to fulfill a single information need. Meanwhile, it is also a common phenomenon to see a user type queries on unrelated topics in a single session. In order to study PubMed users' search strategies, it is necessary to be able to automatically separate unrelated queries and group together related queries. Here, we report a novel approach combining both lexical and contextual analyses for segmenting PubMed query sessions and identifying related queries and compare its performance with the previous approach based solely on concept mapping. We experimented with our integrated approa

SUBMITTER: Lu Z 

PROVIDER: S-EPMC2764279 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature