Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3613
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSoleymanzadeh K.-
dc.contributor.authorKaraoğlan B.-
dc.contributor.authorMetin S.K.-
dc.contributor.authorKişla T.-
dc.date.accessioned2023-06-16T15:00:56Z-
dc.date.available2023-06-16T15:00:56Z-
dc.date.issued2018-
dc.identifier.isbn9.78154E+12-
dc.identifier.urihttps://doi.org/10.1109/SIU.2018.8404633-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/3613-
dc.descriptionAselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netasen_US
dc.description26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- 137780en_US
dc.description.abstractParaphrase identification (PI) is to recognize whether given two sentences are restatements of each other or not. In our study we propose an approach that exploits machine translation and text similarity metrics as features for PI. Machine learning algorithms like Support Vector Machine (SVM) with three different kernels, C4.5 Decision tree and Multinomial Naïve Bayes (NB) are trained with these features. We evaluated our system on Parder, Turkish paraphrase corpus. The experimental results show that the proposed approach offers state-of-the-art results. © 2018 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof26th IEEE Signal Processing and Communications Applications Conference, SIU 2018en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine translation metricsen_US
dc.subjectParaphrase Identificationen_US
dc.subjectText similarity metricsen_US
dc.subjectBarium compoundsen_US
dc.subjectComputational linguisticsen_US
dc.subjectComputer aided language translationen_US
dc.subjectData miningen_US
dc.subjectDecision treesen_US
dc.subjectImage retrievalen_US
dc.subjectLearning algorithmsen_US
dc.subjectSodium compoundsen_US
dc.subjectSupport vector machinesen_US
dc.subjectC4.5 decision treesen_US
dc.subjectMachine translationsen_US
dc.subjectMultinomialsen_US
dc.subjectParaphrase corpusen_US
dc.subjectParaphrase identificationsen_US
dc.subjectState of the arten_US
dc.subjectText similarityen_US
dc.subjectTurkishsen_US
dc.subjectSignal processingen_US
dc.titleCombining machine translation and text similarity metrics to identify paraphrases in Turkishen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/SIU.2018.8404633-
dc.identifier.scopus2-s2.0-85050799621en_US
dc.authorscopusid56246309600-
dc.authorscopusid24471923700-
dc.authorscopusid24314851200-
dc.identifier.startpage1en_US
dc.identifier.endpage4en_US
dc.identifier.wosWOS:000511448500486en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityN/A-
dc.identifier.wosqualityN/A-
item.grantfulltextreserved-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Files in This Item:
File SizeFormat 
2703.pdf
  Restricted Access		275.11 kBAdobe PDFView/Open    Request a copy
Show simple item record



CORE Recommender

Page view(s)

66
checked on Nov 18, 2024

Download(s)

6
checked on Nov 18, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.