RealText-cs - Corpus based domain independent Content Selection model

Date
2014
Authors
Perera, R
Nand, P
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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.

Description
Keywords
Content selection; Natural language generation; Natural Language Processing; Semantic web
Source
In Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on , vol., no., pp.599-606, 10-12 Nov. 2014 doi: 10.1109/ICTAI.2014.95
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