Publication:
Using Probability Theory to Identify the Unsure Value of an Incomplete Sentence

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dc.Conferencecode123957
dc.Conferencedate25 March 2015 through 27 March 2015
dc.Conferencename17th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2015
dc.citedby1
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.affiliationsUniversiti Putra Malaysia (UPM)
dc.contributor.affiliationsUniversiti Tun Hussein Onn Malaysia (UTHM)
dc.contributor.authorNabila N.F.en_US
dc.contributor.authorBasir N.en_US
dc.contributor.authorSaudi M.M.en_US
dc.contributor.authorPitchay S.A.en_US
dc.contributor.authorRidzuan F.en_US
dc.contributor.authorMamat A.en_US
dc.contributor.authorDeris M.M.en_US
dc.date.accessioned2024-05-29T01:55:15Z
dc.date.available2024-05-29T01:55:15Z
dc.date.issued2016
dc.description.abstractMost of the existing techniques on relation extraction focus on extracting relation between subject, predicate and object in a single sentence. However, these techniques unable to handle the situation when the text has sentences that are incomplete: either does not have or unclear subject or object in sentence (i.e. 'unsure' value). Thus this does not properly represent the domain text. This paper proposes an approach to predict and identify the unsure value to complete the sentences in the domain text. The proposed approach is based on the probability theory to identify terms (i.e., subject or object) that are more likely to replace the 'unsure' value. We use voting machine domain text as a case study. � 2015 IEEE.
dc.description.natureFinalen_US
dc.editorCant R.Orsoni A.Saad I.Al-Dabass D.Ibrahim Z.en_US
dc.identifier.ArtNo7576591
dc.identifier.doi10.1109/UKSim.2015.90
dc.identifier.epage501
dc.identifier.isbn9781480000000
dc.identifier.scopus2-s2.0-84991740107
dc.identifier.spage497
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84991740107&doi=10.1109%2fUKSim.2015.90&partnerID=40&md5=aefded7adfea651d0866695d4bd35615
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9661
dc.languageEnglish
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - UKSim-AMSS 17th International Conference on Computer Modelling and Simulation, UKSim 2015
dc.sourceScopus
dc.subjectIncomplete information systemen_US
dc.subjectmost dominanten_US
dc.subjectnon-taxanomicen_US
dc.subjectpredicateen_US
dc.subjectprobability theoryen_US
dc.subjectCircuit simulationen_US
dc.subjectComputer scienceen_US
dc.subjectComputersen_US
dc.subjectSoftware engineeringen_US
dc.subjectIncomplete information systemsen_US
dc.subjectmost dominanten_US
dc.subjectnon-taxanomicen_US
dc.subjectpredicateen_US
dc.subjectProbability theoryen_US
dc.subjectProbabilityen_US
dc.titleUsing Probability Theory to Identify the Unsure Value of an Incomplete Sentence
dc.typeConference Paperen_US
dspace.entity.typePublication

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