Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C. & Nevill-Manning, C. G. (2000). KEA: Practical automatic keyphrase extraction. (Working paper 00/05). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Permanent Research Commons link: http://hdl.handle.net/10289/1021
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.
University of Waikato, Department of Computer Science
- 2000 Working Papers