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      • 2000 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2000 Working Papers
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      KEA: Practical automatic keyphrase extraction

      Witten, Ian H.; Paynter, Gordon W.; Frank, Eibe; Gutwin, Carl; Nevill-Manning, Craig G.
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      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: https://hdl.handle.net/10289/1021
      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.
      Date
      2000-03
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      00/05
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 2000 Working Papers [12]
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