Using Semantic Context to Rank the Results of Keyword Search

Marcus A. Butavicius, Kathryn M. Parsons, Agata McCormac, Simon J. Dennis, Aaron Ceglar, Derek Weber, Lael Ferguson, Kenneth Treharne, Richard Leibbrandt, David Powers

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In an empirical user study, we assessed two approaches to ranking the results from a keyword search using semantic contextual match based on Latent Semantic Analysis. These techniques involved searches initiated from words found in a seed document within a corpus. The first approach used the sentence around the search query in the document as context while the second used the entire document. With a corpus of 20,000 documents and a small proportion of relevant documents (<0.1%), both techniques outperformed a conventional keyword search on a recall-based information retrieval (IR) task. These context-based techniques were associated with a reduction in the number of searches conducted, an increase in users’ precision and, to a lesser extent, an increase in recall. This improvement was strongest when the ranking was based on document, rather than sentence, context. Individuals were more effective on the IR task when the lists returned by the techniques were ranked better. User performance on the task also correlated with achievement on a generalized IQ test but not on a linguistic ability test.

Original languageEnglish
Pages (from-to)725-741
Number of pages17
Issue number9
Early online date9 Jul 2018
Publication statusPublished - 2019


  • Semantic context
  • Keyword searching
  • Latent Semantic Analysis


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