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.

Keyphrases Extraction from Scientific Documents: Improving Machine Learning Approaches with Natural Language Processing

Krapivin, Mikalai;Marchese, Maurizio;Blanzieri, Enrico;Segata, Nicola
2010-01-01

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.
2010
The Role of Digital Libraries in a Time of Global Change
Berlin
Springer Berlin / Heidelberg
9783642136542
Krapivin, Mikalai; A., Autayeu; Marchese, Maurizio; Blanzieri, Enrico; Segata, Nicola
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/89900
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 34
  • ???jsp.display-item.citation.isi??? 16
social impact