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dc.contributor.authorPerera, Ren_NZ
dc.contributor.authorNand, Pen_NZ
dc.date.accessioned2015-10-14T03:03:40Z
dc.date.available2015-10-14T03:03:40Z
dc.date.copyright2014en_NZ
dc.identifier.citationIn Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on , vol., no., pp.599-606, 10-12 Nov. 2014 doi: 10.1109/ICTAI.2014.95en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/9109
dc.description.abstractContent selection is a highly domain dependent task responsible for retrieving relevant information from a knowledge source using a given communicative goal. This paper presents a domain independent content selection model using keywords as communicative goal. We employ DBpedia triple store as our knowledge source and triples are selected based on weights assigned to each triple. The calculation of the weights is carried out through log likelihood distance between a domain corpus and a general reference corpus. The method was evaluated using keywords extracted from QALD dataset and the performance was compared with cross entropy based statistical content selection. The evaluation results showed that the proposed method can perform 32% better than cross entropy based statistical content selection.en_NZ
dc.publisherIEEEen_NZ
dc.relation.urihttp://dx.doi.org/10.1109/ICTAI.2014.95
dc.rightsCopyright © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectContent selection; Natural language generation; Natural Language Processing; Semantic web
dc.titleRealText-cs - Corpus based domain independent Content Selection modelen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1109/ICTAI.2014.95en_NZ
pubs.elements-id189764


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