KEA: Practical Automatic Keyphrase Extraction

by: Ian H Witten, Gordon W Paynter, Eibe Frank, Carl Gutwin, Craig G Nevill-Manning

(5 February 1999)

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arXiv (abstract), arXiv (PDF)

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Abstract

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.

@misc{citeulike:111658, abstract = {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.}, author = {Witten, Ian H. and Paynter, Gordon W. and Frank, Eibe and Gutwin, Carl and Nevill-Manning, Craig G. }, citeulike-article-id = {111658}, eprint = {cs/9902007}, keywords = {information-extraction, keyphrase-extraction, lexical}, month = {February}, posted-at = {2005-03-02 15:44:22}, priority = {2}, title = {KEA: Practical Automatic Keyphrase Extraction}, url = {http://arxiv.org/abs/cs/9902007}, year = {1999} }

RIS record

TY - ELEC ID - citeulike:111658 L3 - citeulike-article-id:111658 TI - KEA: Practical Automatic Keyphrase Extraction N2 - 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. KW - information-extraction KW - keyphrase-extraction KW - lexical AU - Witten, Ian AU - Paynter, Gordon AU - Frank, Eibe AU - Gutwin, Carl AU - Nevill-Manning, Craig PY - 1999/02/05/ UR - http://arxiv.org/abs/cs/9902007 ER -

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