Publication: 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 Witten
University of Waikato
Gordon Paynter
University of Waikato
Eibe Frank
University of Waikato
Carl Gutwin
University of Saskatchewan
Craig Nevill-Manning
Rutgers University


Witten, I., Paynter, G., Frank, E., Gutwin, C., Nevill-Manning, C. 2004. KEA: Practical Automatic Keyphrase Extraction. In In Design and Usability of Digital Libraries: Case Studies in the Asia Pacific, 314-326.


@article {71-kea-practical-automatic,
author= {Ian Witten and Gordon Paynter and Eibe Frank and Carl Gutwin and Craig Nevill-Manning},
title= {KEA: Practical Automatic Keyphrase Extraction},
booktitle= {In Design and Usability of Digital Libraries: Case Studies in the Asia Pacific},
year= {2004},
pages= {314-326}