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Learning Lexical-Semantic Relations Using Intuitive Cognitive Links

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Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

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Abstract

Identifying the specific semantic relations between words is crucial for IR and NLP systems. Our goal in this paper is twofold. First, we want to understand whether learning a classifier for one semantic relation (e.g. hypernymy) can gain from concurrently learning another classifier for a cognitively-linked semantic relation (e.g. co-hyponymy). Second, we evaluate how these systems perform where only few labeled examples exist. To answer the first question, we rely on a multi-task neural network architecture, while for the second we use self-learning to evaluate whether semi-supervision improves performance. Our results on two popular datasets as well as a novel dataset proposed in this paper show that concurrent learning of semantic relations consistently benefits performance. On the other hand, we find that semi-supervised learning can be useful depending on the semantic relation. The code and the datasets are available at https://bit.ly/2Qitasd.

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Notes

  1. 1.

    This issue is out of the scope of this paper.

  2. 2.

    We are aware that this architecture can further be improved by additional task-specific inputs, but as a great deal of possible models can be proposed, which deserve intensive research, this issue remains out of the scope of this paper.

  3. 3.

    https://github.com/esantus/ROOT9.

  4. 4.

    Available at https://bit.ly/2Qitasd.

  5. 5.

    http://wordnetcode.princeton.edu/3.0/.

  6. 6.

    This value was set experimentally.

  7. 7.

    A large number of hypernym pairs contain the root synset “entity”, i.e. path length equals to 0.

  8. 8.

    All datasets are available at https://bit.ly/2Qitasd.

  9. 9.

    A multi-class model learns to separate between several classes and direct comparison with binary models is not fair. Nevertheless, we report its performance as it highlights the potential of multi-class learning for problems that are cognitively similar.

  10. 10.

    The code is available at https://bit.ly/2Qitasd.

  11. 11.

    Column 3 of Table 4.

  12. 12.

    Note that due to the lexical split process, results can not directly be compared to the ones obtained over ROOT9 or RUMEN.

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Author information

Authors and Affiliations

  1. Kelkoo Group, Grenoble, France

    Georgios Balikas

  2. Normandy University, CNRS GREYC, Caen, France

    Gaël Dias & Houssam Akhmouch

  3. Kodar Ltd., Plovdiv, Bulgaria

    Rumen Moraliyski

  4. Credit Agricole Brie Picardie, Amiens, France

    Houssam Akhmouch

  5. University of Grenoble Alps, CNRS LIG, Grenoble, France

    Massih-Reza Amini

Authors
  1. Georgios Balikas
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  2. Gaël Dias
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  3. Rumen Moraliyski
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  4. Houssam Akhmouch
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  5. Massih-Reza Amini
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Corresponding author

Correspondence to Gaël Dias .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Balikas, G., Dias, G., Moraliyski, R., Akhmouch, H., Amini, MR. (2019). Learning Lexical-Semantic Relations Using Intuitive Cognitive Links. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_1

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  • DOI: https://doi.org/10.1007/978-3-030-15712-8_1

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