Machine Learning Based Keyphrase Extraction: Comparing Decision Trees, Naïve Bayes, and Artificial Neural Networks


Kamal Sarkar, Mita Nasipuri, Suranjan Ghose, Journal of Information Processing Systems Vol. 8, No. 4, pp. 693-712, Dec. 2012  

10.3745/JIPS.2012.8.4.693
Keywords: Keyphrase Extraction, Decision Tree, Naive Bayes, Artificial Neural Networks, Machine Learning, WEKA
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Abstract

The paper presents three machine learning based keyphrase extraction methods that respectively use Decision Trees, Naïve 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 Naïve Bayes. The results also show that the Neural Network based method performs better than KEA.


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Cite this article
[APA Style]
Kamal Sarkar, Mita Nasipuri, & Suranjan Ghose (2012). Machine Learning Based Keyphrase Extraction: Comparing Decision Trees, Naïve Bayes, and Artificial Neural Networks. Journal of Information Processing Systems, 8(4), 693-712. DOI: 10.3745/JIPS.2012.8.4.693.

[IEEE Style]
K. Sarkar, M. Nasipuri and S. Ghose, "Machine Learning Based Keyphrase Extraction: Comparing Decision Trees, Naïve Bayes, and Artificial Neural Networks," Journal of Information Processing Systems, vol. 8, no. 4, pp. 693-712, 2012. DOI: 10.3745/JIPS.2012.8.4.693.

[ACM Style]
Kamal Sarkar, Mita Nasipuri, and Suranjan Ghose. 2012. Machine Learning Based Keyphrase Extraction: Comparing Decision Trees, Naïve Bayes, and Artificial Neural Networks. Journal of Information Processing Systems, 8, 4, (2012), 693-712. DOI: 10.3745/JIPS.2012.8.4.693.