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Abstract: Keyphrases are an important means of document summarization, clustering, and topic search. Only a small minority of documents have author-assigned keyphrases, and manually assigning keyphrases to existing documents is very laborious. Therefore it is highly desirable to automate the keyphrase extraction process. This paper shows that a simple procedure for keyphrase extraction based on the naive Bayes learning scheme performs comparably to the state of the art. It goes on to explain how this... (Update)
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BibTeX entry: (Update)
Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C. and Nevill-Manning, C.G. (1999) "Domain-specific keyphrase extraction." Submitted to IJCAI. http://citeseer.ist.psu.edu/frank99domainspecific.html More
@inproceedings{ frank99domainspecific,
author = "Eibe Frank and Gordon W. Paynter and Ian H. Witten and Carl Gutwin and Craig G. Nevill-Manning",
title = "Domain-Specific Keyphrase Extraction",
booktitle = "{IJCAI}",
pages = "668-673",
year = "1999",
url = "citeseer.ist.psu.edu/frank99domainspecific.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan - 1992
657 Bagging predictors - Breiman - 1996
128 the optimality of the simple bayesian classifier under zero-.. - Domingos, Pazzani - 1997
120 Inductive learning algorithms and representations for text c.. (context) - Dumais, Platt et al. - 1998
29 Mechanical translation and computational linguistics (context) - Lovins, algorithm - 1968
28 Multi-interval discretization of continuousvalued attributes.. (context) - Fayyad, Irani - 1993
15 Learning to extract keyphrases from text - Turney - 1999
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