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International Conference on Applications of Natural Language to Information Systems

NLDB 2016: Natural Language Processing and Information Systems pp 276–284Cite as

An Active Ingredients Entity Recogniser System Based on Profiles

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9612))

Abstract

This paper describes an active ingredients named entity recogniser. Our machine learning system, which is language and domain independent, employs unsupervised feature generation and weighting from the training data. The proposed automatic feature extraction process is based on generating a profile for the given entity without traditional knowledge resources (such as dictionaries). Our results (F1 87.3 % [95 %CI: 82.07–92.53]) proves that unsupervised feature generation can achieve a high performance for this task.

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Acknowledgments

This paper has been partially supported by the Spanish Government (grant no. TIN2015-65100-R).

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Authors and Affiliations

  1. Department of Software and Computing Systems, University of Alicante, Alicante, Spain

    Isabel Moreno & Paloma Moreda

  2. Department of Nursing, University of Alicante, Alicante, Spain

    M. T. Romá-Ferri

Authors
  1. Isabel Moreno
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  2. Paloma Moreda
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  3. M. T. Romá-Ferri
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Corresponding author

Correspondence to Isabel Moreno .

Editor information

Editors and Affiliations

  1. ConservatoireNational desArts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, United Kingdom

    Farid Meziane

  3. University of Salford, Salford, United Kingdom

    Mohamad Saraee

  4. Oakland University, Rochester, Michigan, USA

    Vijayan Sugumaran

  5. University of Salford, Salford, United Kingdom

    Sunil Vadera

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© 2016 Springer International Publishing Switzerland

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Moreno, I., Moreda, P., Romá-Ferri, M.T. (2016). An Active Ingredients Entity Recogniser System Based on Profiles. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_25

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  • DOI: https://doi.org/10.1007/978-3-319-41754-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41753-0

  • Online ISBN: 978-3-319-41754-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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