Extracting Significant Phrases from Text

229. Y. J. Lui, R. P. Brent and A. Calinescu, Extracting significant phrases from text, Proc. 21st International Conference on Advanced Information Networking and Applications (AINA 2007), Workshop Proceedings (AINAW07), Vol. 1, May 2007, 361-366.

Preprint: pdf (87K).

Paper: available from IEEE CS Digital Library.


Prospective readers can quickly determine whether a document is relevant to their information need if the significant phrases (or keyphrases) in this document are provided. Although keyphrases are useful, not many documents have keyphrases assigned to them, and manually assigning keyphrases to existing documents is costly. Therefore, there is a need for automatic keyphrase extraction. This paper introduces a new domain-independent keyphrase extraction algorithm. The algorithm approaches the problem of keyphrase extraction as a classification task, and uses a combination of statistical and computational linguistics techniques, a new set of attributes, and a new learning method, to distinguish keyphrases from non-keyphrases. Experiments indicate that this algorithm performs at least as well as other keyphrase extraction tools and that it outperforms Microsoft Word 2000's AutoSummarize feature significantly.

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