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      Scientific Table Search Using Keyword Queries

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          Abstract

          Tables are common and important in scientific documents, yet most text-based document search systems do not capture structures and semantics specific to tables. How to bridge different types of mismatch between keywords queries and scientific tables and what influences ranking quality needs to be carefully investigated. This paper considers the structure of tables and gives different emphasis to table components. On the query side, thanks to external knowledge such as knowledge bases and ontologies, key concepts are extracted and used to build structured queries, and target quantity types are identified and used to expand original queries. A probabilistic framework is proposed to incorporate structural and semantic information from both query and table sides. We also construct and release TableArXiv, a high quality dataset with 105 queries and corresponding relevance judgements for scientific table search. Experiments demonstrate significantly higher accuracy overall and at the top of the rankings than several baseline methods.

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          Discovering key concepts in verbose queries

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            Learning concept importance using a weighted dependence model

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              Effective query formulation with multiple information sources

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                Author and article information

                Journal
                2017-07-11
                Article
                1707.03423
                c85e8a0a-6337-4bf5-8694-1d4be3c24aa0

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.IR

                Information & Library science
                Information & Library science

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