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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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Real Text-CS - Corpus based domain independent content selection model

Perera, R; Nand, P
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ICTAI2014.pdf (662.7Kb)
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http://hdl.handle.net/10292/8596
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Abstract
Content 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.
Keywords
Content selection; Natural Language Generation; Natural Language Processing; Semantic web
Date
November 2014
Source
Published in: IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp.599 - 606
Item Type
Conference Contribution
Publisher
IEEE
DOI
10.1109/ICTAI.2014.95
Publisher's Version
http://dx.doi.org/10.1109/ICTAI.2014.95
Rights Statement
Copyright © 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.

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