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1999
Springer

KEA: Practical Automatic Keyphrase Extraction

12 years 1 months ago
KEA: Practical Automatic Keyphrase Extraction
Keyphrases provide semantic metadata that summarize and characterize documents. This paper describes Kea, an algorithm for automatically extracting keyphrases from text. Kea identifies candidate keyphrases using lexical methods, calculates feature values for each candidate, and uses a machine-learning algorithm to predict which candidates are good keyphrases. The machine learning scheme first builds a prediction model using training documents with known keyphrases, and then uses the model to find keyphrases in new documents. We use a large test corpus to evaluate Kea’s effectiveness in terms of how many author-assigned keyphrases are correctly identified. The system is simple, robust, and publicly available.
Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Where DL
Authors Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl Gutwin, Craig G. Nevill-Manning
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