Improving educational web search for question-like queries through subject classification

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buir.contributor.authorUlusoy, Özgür
dc.citation.epage246en_US
dc.citation.issueNumber1en_US
dc.citation.spage228en_US
dc.citation.volumeNumber56en_US
dc.contributor.authorYılmaz, Tolgaen_US
dc.contributor.authorÖzcan, R.en_US
dc.contributor.authorAltıngövde, İ. Şen_US
dc.contributor.authorUlusoy, Özgüren_US
dc.date.accessioned2020-02-04T06:26:34Z
dc.date.available2020-02-04T06:26:34Z
dc.date.issued2019
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractStudents use general web search engines as their primary source of research while trying to find answers to school-related questions. Although search engines are highly relevant for the general population, they may return results that are out of educational context. Another rising trend; social community question answering websites are the second choice for students who try to get answers from other peers online. We attempt discovering possible improvements in educational search by leveraging both of these information sources. For this purpose, we first implement a classifier for educational questions. This classifier is built by an ensemble method that employs several regular learning algorithms and retrieval based approaches that utilize external resources. We also build a query expander to facilitate classification. We further improve the classification using search engine results and obtain 83.5% accuracy. Although our work is entirely based on the Turkish language, the features could easily be mapped to other languages as well. In order to find out whether search engine ranking can be improved in the education domain using the classification model, we collect and label a set of query results retrieved from a general web search engine. We propose five ad-hoc methods to improve search ranking based on the idea that the query-document category relation is an indicator of relevance. We evaluate these methods for overall performance, varying query length and based on factoid and non-factoid queries. We show that some of the methods significantly improve the rankings in the education domain.en_US
dc.description.provenanceSubmitted by Onur Emek (onur.emek@bilkent.edu.tr) on 2020-02-04T06:26:34Z No. of bitstreams: 1 Bilkent-research-paper.pdf: 268963 bytes, checksum: ad2e3a30c8172b573b9662390ed2d3cf (MD5)en
dc.description.provenanceMade available in DSpace on 2020-02-04T06:26:34Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 268963 bytes, checksum: ad2e3a30c8172b573b9662390ed2d3cf (MD5) Previous issue date: 2018en
dc.embargo.release2022-01-01
dc.identifier.doi10.1016/j.ipm.2018.10.013en_US
dc.identifier.issn0306-4573
dc.identifier.urihttp://hdl.handle.net/11693/53023
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttps://dx.doi.org/10.1016/j.ipm.2018.10.013en_US
dc.source.titleInformation Processing and Managementen_US
dc.subjectEducational web searchen_US
dc.subjectQuestion classificationen_US
dc.subjectSearch engine result page rankingen_US
dc.subjectK-12en_US
dc.titleImproving educational web search for question-like queries through subject classificationen_US
dc.typeArticleen_US

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