Using Semantic Context to Rank the Results of Keyword Search. Issue 9 (28th May 2019)

• Record Type: Journal Article
• Authors: Butavicius, Marcus A.; Parsons, Kathryn M.; McCormac, Agata; Dennis, Simon J.; Ceglar, Aaron; Weber, Derek; Ferguson, Lael; Treharne, Kenneth; Leibbrandt, Richard; Powers, David
• Is Part Of: International journal of human-computer interaction. Volume 35:Issue 9(2019)
• Page Start: 725
• Page End: 741
• Publication Date: 2019-05-28
• Subjects: Human-computer interaction -- Periodicals; 004.01905
• DOI: 10.1080/10447318.2018.1485263
• Languages: English
• ISSNs: 1044-7318

Abstract

ABSTRACT: 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.