University Bretagne Sud, IRISA Lab, France – Vannes Email: ahmad.issa-alaa-eddine@univ-ubs.fr, giuseppe.berio@univ-ubs.fr, nicolas.bechet@irisa.fr"/> LINA - University of Nantes, France E-mail: mounira.harzallah@univ-nantes.fr"/> Lebanese University, Lebanon Email: ahmad.faour@ul.edu.lb"/>
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2021 Volume 36
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RESEARCH ARTICLE   Open Access    

A 3-phase approach based on sequential mining and dependency parsing for enhancing hypernym patterns performance

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  • Abstract: Patterns have been extensively used to extract hypernym relations from texts. The most popular patterns are Hearst’s patterns, formulated as regular expressions mainly based on lexical information. Experiences have reported good precision and low recall for such patterns. Thus, several approaches have been developed for improving recall. While these approaches perform better in terms of recall, it remains quite difficult to further increase recall without degrading precision. In this paper, we propose a novel 3-phase approach based on sequential pattern mining to improve pattern-based approaches in terms of both precision and recall by (i) using a rich pattern representation based on grammatical dependencies (ii) discovering new hypernym patterns, and (iii) extending hypernym patterns with anti-hypernym patterns to prune wrong extracted hypernym relations. The results obtained by performing experiments on three corpora confirm that using our approach, we are able to learn sequential patterns and combine them to outperform existing hypernym patterns in terms of precision and recall. The comparison to unsupervised distributional baselines for hypernym detection shows that, as expected, our approach yields much better performance. When compared to supervised distributional baselines for hypernym detection, our approach can be shown to be complementary and much less loosely coupled with training datasets and corpora.
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  • Cite this article

    Ahmad Issa Alaa Aldine, Mounira Harzallah, Giuseppe Berio, Nicolas Béchet, Ahmad Faour. 2021. A 3-phase approach based on sequential mining and dependency parsing for enhancing hypernym patterns performance. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000126
    Ahmad Issa Alaa Aldine, Mounira Harzallah, Giuseppe Berio, Nicolas Béchet, Ahmad Faour. 2021. A 3-phase approach based on sequential mining and dependency parsing for enhancing hypernym patterns performance. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000126
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RESEARCH ARTICLE   Open Access    

A 3-phase approach based on sequential mining and dependency parsing for enhancing hypernym patterns performance

  • Received: 18 July 2020
  • Revised: 03 August 2021
  • Accepted: 20 August 2021
  • Published online: 22 September 2021
The Knowledge Engineering Review  36,  2021, 36(1)  |  Cite this article

Abstract: Abstract: Patterns have been extensively used to extract hypernym relations from texts. The most popular patterns are Hearst’s patterns, formulated as regular expressions mainly based on lexical information. Experiences have reported good precision and low recall for such patterns. Thus, several approaches have been developed for improving recall. While these approaches perform better in terms of recall, it remains quite difficult to further increase recall without degrading precision. In this paper, we propose a novel 3-phase approach based on sequential pattern mining to improve pattern-based approaches in terms of both precision and recall by (i) using a rich pattern representation based on grammatical dependencies (ii) discovering new hypernym patterns, and (iii) extending hypernym patterns with anti-hypernym patterns to prune wrong extracted hypernym relations. The results obtained by performing experiments on three corpora confirm that using our approach, we are able to learn sequential patterns and combine them to outperform existing hypernym patterns in terms of precision and recall. The comparison to unsupervised distributional baselines for hypernym detection shows that, as expected, our approach yields much better performance. When compared to supervised distributional baselines for hypernym detection, our approach can be shown to be complementary and much less loosely coupled with training datasets and corpora.

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    • In Snow et al. (2005), paths are derived from Minipar parser (Lin 2003) which is a shallow parser; while in Sheena et al. (2016), paths are derived from Stanford parser (Klein & Manning 2003) which is a dependency parser.

    • For instance, in noun phrases ‘musical instrument’ and ‘the title of book’, ‘instrument’ and ‘title’ are the respective headwords.

    • Blank cells are those corresponding to patterns that are not learned by the corpus.

    • Word2Vec is available in Gensim python library.

    • Number 20 has been chosen by observing unsuccessful tests to extract good patterns from TS with less than 20 sentences; additionally, few sentences can be manually analyzed if needed.

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    • © The Author(s), 2021. Published by Cambridge University Press on behalf of Asian Journal of Law and Society2021Cambridge University Press
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    Ahmad Issa Alaa Aldine, Mounira Harzallah, Giuseppe Berio, Nicolas Béchet, Ahmad Faour. 2021. A 3-phase approach based on sequential mining and dependency parsing for enhancing hypernym patterns performance. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000126
    Ahmad Issa Alaa Aldine, Mounira Harzallah, Giuseppe Berio, Nicolas Béchet, Ahmad Faour. 2021. A 3-phase approach based on sequential mining and dependency parsing for enhancing hypernym patterns performance. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000126
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