Keyphrases extraction from scientific documents: improving machine learning approaches with natural language processing

Authors:
Mikalai Krapivin;Aliaksandr Autayeu;Maurizio Marchese;Enrico Blanzieri;Nicola Segata
Affiliations:
DISI, University of Trento, Italy;DISI, University of Trento, Italy;DISI, University of Trento, Italy;DISI, University of Trento, Italy;DISI, University of Trento, Italy
Venue:
ICADL'10 Proceedings of the role of digital libraries in a time of global change, and 12th international conference on Asia-Pacific digital libraries
Year:
2010

Citing 14
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Abstract

In this paper we use Natural Language Processing techniques to improve different machine learning approaches (Support Vector Machines (SVM), Local SVM, Random Forests) to the problem of automatic keyphrases extraction from scientific papers. For the evaluation we propose a large and high-quality dataset: 2000 ACM papers from the Computer Science domain. We evaluate by comparison with expert-assigned keyphrases. Evaluation shows promising results that outperform state-of-the-art Bayesian learning system KEA improving the average F-Measure from 22% (KEA) to 30% (Random Forest) on the same dataset without the use of controlled vocabularies. Finally, we report a detailed analysis of the effect of the individual NLP features and data set size on the overall quality of extracted keyphrases.