Real Text-CS - Corpus based domain independent content selection model

Perera, R
Nand, P
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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.

Content selection , Natural Language Generation , Natural Language Processing , Semantic web
Published in: IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp.599 - 606
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