Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Machine Learning Based Keyphrase Extraction: Comparing Decision Trees, Naïve Bayes, and Artificial Neural Networks

  • Sarkar, Kamal (Dept. of Computer Science and Engineering, Jadavpur University) ;
  • Nasipuri, Mita (Dept. of Computer Science and Engineering, Jadavpur University) ;
  • Ghose, Suranjan (Dept. of Computer Science and Engineering, Jadavpur University)
  • Received : 2012.07.02
  • Accepted : 2012.10.05
  • Published : 2012.12.31
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Abstract

The paper presents three machine learning based keyphrase extraction methods that respectively use Decision Trees, Na$\ddot{i}$ve Bayes, and Artificial Neural Networks for keyphrase extraction. We consider keyphrases as being phrases that consist of one or more words and as representing the important concepts in a text document. The three machine learning based keyphrase extraction methods that we use for experimentation have been compared with a publicly available keyphrase extraction system called KEA. The experimental results show that the Neural Network based keyphrase extraction method outperforms two other keyphrase extraction methods that use the Decision Tree and Na$\ddot{i}$ve Bayes. The results also show that the Neural Network based method performs better than KEA.

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