Abstract
Keyphrases can provide a brief summary of documents. Keyphrase extraction, defined as automatic selection of important phrases within the body of a document, is important in some fields. Generally the keyphrase extraction process is seen as a classification task, where feature selection and learning model are the key problems. In this paper, different shallow features are surveyed and the commonly used learning methods are compared. The experimental results demonstrate that the detailed survey of shallow features plus a simpler method can more enhance the extraction performance.
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© 2009 Springer-Verlag Berlin Heidelberg
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Wang, C., Li, S., Wang, W. (2009). Experiment Research on Feature Selection and Learning Method in Keyphrase Extraction. In: Li, W., Mollá-Aliod, D. (eds) Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy. ICCPOL 2009. Lecture Notes in Computer Science(), vol 5459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00831-3_29
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DOI: https://doi.org/10.1007/978-3-642-00831-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00830-6
Online ISBN: 978-3-642-00831-3
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