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Exploiting extra-textual and linguistic information in keyphrase extraction

Published online by Cambridge University Press:  30 September 2014

GÁBOR BEREND
GÁBOR BEREND*
Affiliation:
University of Szeged, Department of Informatics, Árpád tér 2, Szeged, H6720, Hungary email: berendg@inf.u-szeged.hu
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Abstract

Keyphrases are the most important phrases of documents that make them suitable for improving natural language processing tasks, including information retrieval, document classification, document visualization, summarization and categorization. Here, we propose a supervised framework augmented by novel extra-textual information derived primarily from Wikipedia. Wikipedia is utilized in such an advantageous way that – unlike most other methods relying on Wikipedia – a full textual index of all the Wikipedia articles is not required by our approach, as we only exploit the category hierarchy and a list of multiword expressions derived from Wikipedia. This approach is not only less resource intensive, but also produces comparable or superior results compared to previous similar works. Our thorough evaluations also suggest that the proposed framework performs consistently well on multiple datasets, being competitive or even outperforming the results obtained by other state-of-the-art methods. Besides introducing features that incorporate extra-textual information, we also experimented with a novel way of representing features that are derived from the POS tagging of the keyphrase candidates.

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 1 , January 2016 , pp. 73 - 95
Copyright
Copyright © Cambridge University Press 2014 

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