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dc.contributor.authorMakary, Mireille
dc.contributor.authorOakes, Michael
dc.contributor.authorYamout, Fadi
dc.date.accessioned2017-03-09T10:49:27Z
dc.date.available2017-03-09T10:49:27Z
dc.date.issued2016-09-30
dc.identifier.citationMakary, M., Oakes, M., Yamout, F. (2016) 'Using key phrases as new queries in building relevance judgements automatically', Lernen,Wissen, Daten, Analysen (LWDA) Conference Proceedings, LWDA'16, September 12-14, Potsdam, Germany: CEUR - workshop proceedings, 1670, pp. 175-176.
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/2436/620408
dc.description.abstractWe describe a new technique for building a relevance judgment list (qrels) for TREC test collections with no human intervention. For each TREC topic, a set of new queries is automatically generated from key phrases extract-ed from the top k documents retrieved from 12 different Terrier weighting models when the initial TREC topic is submitted. We assign a score to each key phrase based on its similarity to the original TREC topic. The key phrases with the highest scores become the new queries for a second search, this time using the Terrier BM25 weighting model. The union of the documents retrieved forms the automatically-build set of qrels.
dc.language.isoen
dc.publisherCEUR - workshop proceedings
dc.relation.urlhttp://ceur-ws.org/Vol-1670/
dc.subjectInformation Retrieval
dc.subjectKey Phrases
dc.subjectTest Collections
dc.titleUsing key phrases as new queries in building relevance judgements automatically
dc.typeConference contribution
dc.identifier.journalProceedings of the Conference "Lernen, Wissen, Daten, Analysen"
dc.conference.nameLernen,Wissen, Daten, Analysen (LWDA) Conference Prceedings
pubs.finish-date2016-09-14
pubs.place-of-publicationPotsdam, Germany
pubs.start-date2016-09-12
dc.date.accepted2016-07-01
rioxxterms.funderUniversity of Wolverhampton
rioxxterms.identifier.projectUoW090317MO
rioxxterms.versionAM
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0
rioxxterms.licenseref.startdate2017-03-09
refterms.dateFCD2018-10-18T15:47:00Z
refterms.versionFCDAM
refterms.dateFOA2017-03-09T00:00:00Z
html.description.abstractWe describe a new technique for building a relevance judgment list (qrels) for TREC test collections with no human intervention. For each TREC topic, a set of new queries is automatically generated from key phrases extract-ed from the top k documents retrieved from 12 different Terrier weighting models when the initial TREC topic is submitted. We assign a score to each key phrase based on its similarity to the original TREC topic. The key phrases with the highest scores become the new queries for a second search, this time using the Terrier BM25 weighting model. The union of the documents retrieved forms the automatically-build set of qrels.


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