Abstract
Keyphrases provide semantic metadata that summarizes the documents and enable the reader to quickly determine whether the given article is in the reader’s fields of interest. This paper presents an automatic keyphrase extraction method based on the naive Bayesian learning that exploits a number of domain-specific features to boost up the keyphrase extraction performance in medical domain. The proposed method has been compared to a popular keyphrase extraction algorithm, called Kea.
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Sarkar, K. (2009). Automatic Keyphrase Extraction from Medical Documents. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_44
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DOI: https://doi.org/10.1007/978-3-642-11164-8_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11163-1
Online ISBN: 978-3-642-11164-8
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