Permanent link to Research Commons versionhttps://hdl.handle.net/10289/16395
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 available under the GNU General Public License; the paper gives instructions for use.
Information Science Publishing
This is an author’s accepted version of a chapter published in the book: Design and Usability of Digital Libraries: Case Studies in the Asia Pacific. © 2005 Information Science Publishing.