Quantifying the impact of concept recognition on biomedical information retrieval

  • Authors:
  • Sarvnaz Karimi;Justin Zobel;Falk Scholer

  • Affiliations:
  • NICTA, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville, VIC 3010, Australia;NICTA, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville, VIC 3010, Australia;School of Computer Science and Information Technology, RMIT University, Melbourne, VIC 3000, Australia

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

In ad hoc querying of document collections, current approaches to ranking primarily rely on identifying the documents that contain the query terms. Methods such as query expansion, based on thesaural information or automatic feedback, are used to add further terms, and can yield significant though usually small gains in effectiveness. Another approach to adding terms, which we investigate in this paper, is to use natural language technology to annotate - and thus disambiguate - key terms by the concept they represent. Using biomedical research documents, we quantify the potential benefits of tagging users' targeted concepts in queries and documents in domain-specific information retrieval. Our experiments, based on the TREC Genomics track data, both on passage and full-text retrieval, found no evidence that automatic concept recognition in general is of significant value for this task. Moreover, the issues raised by these results suggest that it is difficult for such disambiguation to be effective.