Abstract.
As math becomes available in digital libraries and on the Web, math search has been receiving some research attention. To be effective and useful, math search systems must not only recognize math symbols and structures in queries and contents, but also present the search hits in a form that enables the user to identify quickly the truly relevant hits. To meet the latter requirement, the hits must be sorted according to domain-appropriate relevance criteria, and each hit ought to be accompanied with a query-relevant brief description, or summary, of its target.
In conventional text information retrieval systems, hits are ranked using relevance metrics that rely mostly on keyword frequencies and document sizes. Such metrics are inadequate in math search. Therefore, new relevance measures must be defined, which take into account math-specific factors. In this paper, new relevance metrics are defined for math search, methods for computing and implementing them are discussed, and comparative performance evaluation results are presented.
Query-relevant hit-summary generation is another factor that enables users to quickly determine the relevance of the presented hits. Hit titles accompanied by several leading sentences from the target document are often inadequate to convey to the user the relevant contents of the hit. This paper presents alternative query-relevant hit-summary generation methods, outlines implementation strategies, and presents performance evaluation results.
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This work was done in part at the National Institute of Standards and Technology, USA, as part of the DLMF Project.
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Youssef, A.S. Relevance Ranking and Hit Description in Math Search. Math.Comput.Sci. 2, 333–353 (2008). https://doi.org/10.1007/s11786-008-0057-3
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DOI: https://doi.org/10.1007/s11786-008-0057-3