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Applying Dependency Relations to Definition Extraction

  • Conference paper
Natural Language Processing and Information Systems (NLDB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8455))

Abstract

Definition Extraction (DE) is the task to automatically identify definitional knowledge in naturally-occurring text. This task has applications in ontology generation, glossary creation or question answering. Although the traditional approach to DE has been based on hand-crafted pattern-matching rules, recent methods incorporate learning algorithms in order to classify sentences as definitional or non-definitional. This paper presents a supervised approach to Definition Extraction in which only syntactic features derived from dependency relations are used. We model the problem as a classification task where each sentence has to be classified as being or not definitional. We compare our results with two well-known approaches: First, a supervised method based on Word-Class Lattices and second, an unsupervised approach based on mining recurrent patterns. Our competitive results suggest that syntactic information alone can contribute substantially to the development and improvement of DE systems.

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Authors and Affiliations

  1. Tractament Automàtic del Llenguatge Natural, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Spain

    Luis Espinosa-Anke & Horacio Saggion

Authors
  1. Luis Espinosa-Anke
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  2. Horacio Saggion
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Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Computer Science,, 2 rue Conté, 75003, Paris, France

    Elisabeth Métais

  2. Cirad, TETIS, 500 rue J.F. Breton, 34093, Montpellier Cedex 5, France

    Mathieu Roche

  3. Irstea, TETIS, 500 rue J.F. Breton, 34093, Montpellier Cedex 5, France

    Maguelonne Teisseire

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Espinosa-Anke, L., Saggion, H. (2014). Applying Dependency Relations to Definition Extraction. In: Métais, E., Roche, M., Teisseire, M. (eds) Natural Language Processing and Information Systems. NLDB 2014. Lecture Notes in Computer Science, vol 8455. Springer, Cham. https://doi.org/10.1007/978-3-319-07983-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-07983-7_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07982-0

  • Online ISBN: 978-3-319-07983-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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