DHPs: Dependency Hearst’s Patterns for Hypernym Relation Extraction

  • Ahmad Issa Alaa AldineEmail author
  • Mounira Harzallah
  • Giuseppe Berio
  • Nicolas Béchet
  • Ahmad Faour
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1222)


Hearst’s patterns are lexico-syntactic patterns that have been extensively used to extract hypernym relations from texts. They are defined as regular expressions based on lexical and syntactical information of each word. Here, we propose a new formulation of Hearst’s patterns using dependency parser, called Dependency Hearst’s Patterns (DHPs). They are defined as dependency patterns based on dependency relations between words. This formulation allows us to define more generic Hearst’s patterns that match better complex or ambiguous sentences. To evaluate our proposal, we have compared the performance of Dependency Hearst’s patterns to lexico-syntactic patterns: Hearst’s patterns and an extended set of Hearst’s patterns applied on two corpora: Music and English. Dependency Hearst’s patterns yield to a considerable improve in term of recall and a slight decrease in term of precision.


Dependency Hearst’s Patterns Hypernym relation extraction Dependency relations 


  1. 1.
    Baroni, M., Bernardi, R., Do, N.Q., Shan, C.C.: Entailment above the word level in distributional semantics. In: EACL, pp. 23–32 (2012)Google Scholar
  2. 2.
    Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: an overview. In: Ontology Learning from Text: Methods, Applications and Evaluation, pp. 3–12 (2005)Google Scholar
  3. 3.
    Camacho-Collados, J., et al.: SemEval-2018 Task 9: hypernym discovery. In: Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval 2018). Association for Computational Linguistics, New Orleans (2018)Google Scholar
  4. 4.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)CrossRefGoogle Scholar
  5. 5.
    Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th International Conference on Computational Linguistics, pp. 539–545 (1992)Google Scholar
  6. 6.
    Aldine, A.I.A., Harzallah, M., Berio, G., Bechet, N., Faour, A.: Redefining Hearst patterns by using dependency relations. In: Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, pp. 148–155. INSTICC, SciTePress (2018).
  7. 7.
    Jacques, M.P., Aussenac-Gilles, N.: Variabilité des performances des outils de tal et genre textuel. Cas des patrons lexico-syntaxiques 47, 11–32 (2006)Google Scholar
  8. 8.
    Kamel, M., dos Santos, C.T., Ghamnia, A., Aussenac-Gilles, N., Fabre, C.: Extracting hypernym relations from Wikipedia disambiguation pages: comparing symbolic and machine learning approaches. In: IWCS (2017)Google Scholar
  9. 9.
    Klaussner, C., Zhekova, D.: Pattern-based ontology construction from selected Wikipedia pages, pp. 103–108 (2011)Google Scholar
  10. 10.
    Kotlerman, L., Dagan, I., Szpektor, I., Zhitomirsky-Geffet, M.: Directional distributional similarity for lexical inference. NLE 16, 359–389 (2010)Google Scholar
  11. 11.
    Marneffe, M.C.D., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC 2006), pp. 449–454 (2006)Google Scholar
  12. 12.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119 (2013)Google Scholar
  13. 13.
    Nakashole, N., Weikum, G., Suchanek, F.: PATTY: a taxonomy of relational patterns with semantic types. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012), pp. 1135–1145. Association for Computational Linguistics, Stroudsburg (2012).
  14. 14.
    Orna-Montesinos, C.: Words and patterns: Lexico-grammatical patterns and semantic relations in domain-specific discourses, vol. 24, Jan 2011Google Scholar
  15. 15.
    Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)Google Scholar
  16. 16.
    Ponzetto, S.P., Strube, M.: Taxonomy induction based on a collaboratively built knowledge repository. Artif. Intell. 175(9), 1737–1756 (2011).
  17. 17.
    Ritter, A., Soderland, S., Etzioni, O.: What is this, anyway: automatic hypernym discovery. In: AAAI Spring Symposium - Technical Report, pp. 88–93, Jan 2009Google Scholar
  18. 18.
    Roller, S., Erk, K., Boleda, G.: Inclusive yet selective: supervised distributional hypernymy detection. In: COLING, pp. 1025–1036 (2014)Google Scholar
  19. 19.
    Roller, S., Kiela, D., Nickel, M.: Hearst patterns revisited: Automatic hypernym detection from large text corpora. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 358–363. Association for Computational Linguistics (2018).
  20. 20.
    Sang, E.T.K., Hofmann, K.: Lexical patterns or dependency patterns: which is better for hypernym extraction? In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009), pp. 174–182. Association for Computational Linguistics, Stroudsburg (2009)Google Scholar
  21. 21.
    Schuster, S., Manning, C.D.: Enhanced English universal dependencies: an improved representation for natural language understanding tasks. In: LREC (2016)Google Scholar
  22. 22.
    Seitner, J., et al.: A large database of hypernymy relations extracted from the web. In: LREC (2016)Google Scholar
  23. 23.
    Sheena, N., Jasmine, S.M., Joseph, S.: Automatic extraction of hypernym and meronym relations in English sentences using dependency parser. Procedia Comput. Sci. 93, 539–546 (2016)CrossRefGoogle Scholar
  24. 24.
    Snow, R., Jurafsky, D., Ng, A.: Learning Syntactic Patterns for Automatic Hypernym Discovery, pp. 1297–1304. MIT Press, Cambridge (2005)Google Scholar
  25. 25.
    Weeds, J., Clarke, D., Reffin, J., Weir, D., Keller, B.: Learning to distinguish hypernyms and co-hyponyms. In: COLING, pp. 2249–2259 (2014)Google Scholar
  26. 26.
    Weeds, J., Weir, D.: A general framework for distributional similarity. In: EMLP, pp. 81–88 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ahmad Issa Alaa Aldine
    • 1
    • 3
    Email author
  • Mounira Harzallah
    • 2
  • Giuseppe Berio
    • 1
  • Nicolas Béchet
    • 1
  • Ahmad Faour
    • 3
  1. 1.University Bretagne Sud, IRISA LabVannesFrance
  2. 2.Nantes University, LS2N LabNantesFrance
  3. 3.Lebanese UniversityBeirutLebanon

Personalised recommendations