keyphrase extraction
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.
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We crawled large amounts of biomedical articles from PubMed for the keyphrase extraction system evaluation.
The articles, that consist of title, abstract and keyphrases provided by the authors, are used for the experiments.
In our paper, cancer-related biomedical articles are selected.
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