|KEA: Practical automatic keyphrase extraction|
Witten, I. H., Paynter, G. W., Frank, E., Gutwin, C., Nevill-Manning, C. G. (1999) Proc Fourth ACM Conference on Digital Libraries,edited by E.A. Fox and N. Rowe, Berkeley, CA, August, pp 254-255. ACM.
Keyphrases provide semantic metadata that summarize and characterize documents. Kea is an algorithm for automatically extracting keyphrases from text. We use a large text corpus to evaluate its effectiveness in terms of how many author-assigned keyphrases are correctly identified. The system is simple, robust, and publicly available. 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.