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Beyond the Bag of Words: A Text Representation for Sentence Selection

  • Conference paper
Advances in Artificial Intelligence (Canadian AI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4013))

Abstract

Sentence selection shares some but not all the characteristics of Automatic Text Categorization. Therefore some but not all the same techniques should be used. In this paper we study a syntactic and semantic enriched text representation for the sentence selection task in a genomics corpus. We show that using technical dictionaries and syntactic relations is beneficial for our problem when using state of the art machine learning algorithms. Furthermore, the syntactic relations can be used by a first order rule learner to obtain even better performance.

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Author information

Authors and Affiliations

  1. School of Information Technology and Engineering., University of Ottawa, Ottawa, Ontario, K1N 6N5

    Maria Fernanda Caropreso & Stan Matwin

  2. Institute for Computer Science, Polish Academy of Science, Wavsaw

    Stan Matwin

Authors
  1. Maria Fernanda Caropreso
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  2. Stan Matwin
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Editor information

Editors and Affiliations

  1. Departement of Computer Science and Software Engineering, Laval University, G1K 7P4, Québec, Canada

    Luc Lamontagne

  2. Département IFT-GLO, Pavillon Adrien-Pouliot, Université Laval, G1K-7P4, Québec, Canada

    Mario Marchand

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© 2006 Springer-Verlag Berlin Heidelberg

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Caropreso, M.F., Matwin, S. (2006). Beyond the Bag of Words: A Text Representation for Sentence Selection. In: Lamontagne, L., Marchand, M. (eds) Advances in Artificial Intelligence. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_28

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  • DOI: https://doi.org/10.1007/11766247_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34628-9

  • Online ISBN: 978-3-540-34630-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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