Skip to main content

DHPs: Dependency Hearst’s Patterns for Hypernym Relation Extraction

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1222))

Abstract

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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    we select 10 sentences.

  2. 2.

    https://stanfordnlp.github.io/CoreNLP/.

  3. 3.

    These rules are language dependent, they are defined for English. Thus, they should be adapted to be used for other languages (e.g French).

  4. 4.

    https://github.com/AhmadIssaAlaa/Dependency-Hearsts-Patterns.

  5. 5.

    The extHP contains all 59 patterns mentioned in the work of Seitner et al. [22].

References

  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. 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. 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. Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)

    Book  Google Scholar 

  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. 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). https://doi.org/10.5220/0006962201480155

  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. 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. Klaussner, C., Zhekova, D.: Pattern-based ontology construction from selected Wikipedia pages, pp. 103–108 (2011)

    Google Scholar 

  10. Kotlerman, L., Dagan, I., Szpektor, I., Zhitomirsky-Geffet, M.: Directional distributional similarity for lexical inference. NLE 16, 359–389 (2010)

    Google Scholar 

  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. 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. 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). http://dl.acm.org/citation.cfm?id=2390948.2391076

  14. Orna-Montesinos, C.: Words and patterns: Lexico-grammatical patterns and semantic relations in domain-specific discourses, vol. 24, Jan 2011

    Google Scholar 

  15. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)

    Google Scholar 

  16. Ponzetto, S.P., Strube, M.: Taxonomy induction based on a collaboratively built knowledge repository. Artif. Intell. 175(9), 1737–1756 (2011). https://doi.org/10.1016/j.artint.2011.01.003. http://www.sciencedirect.com/science/article/pii/S000437021100004X

  17. Ritter, A., Soderland, S., Etzioni, O.: What is this, anyway: automatic hypernym discovery. In: AAAI Spring Symposium - Technical Report, pp. 88–93, Jan 2009

    Google Scholar 

  18. Roller, S., Erk, K., Boleda, G.: Inclusive yet selective: supervised distributional hypernymy detection. In: COLING, pp. 1025–1036 (2014)

    Google Scholar 

  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). http://aclweb.org/anthology/P18-2057

  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. Schuster, S., Manning, C.D.: Enhanced English universal dependencies: an improved representation for natural language understanding tasks. In: LREC (2016)

    Google Scholar 

  22. Seitner, J., et al.: A large database of hypernymy relations extracted from the web. In: LREC (2016)

    Google Scholar 

  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)

    Article  Google Scholar 

  24. Snow, R., Jurafsky, D., Ng, A.: Learning Syntactic Patterns for Automatic Hypernym Discovery, pp. 1297–1304. MIT Press, Cambridge (2005)

    Google Scholar 

  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. Weeds, J., Weir, D.: A general framework for distributional similarity. In: EMLP, pp. 81–88 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmad Issa Alaa Aldine .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Issa Alaa Aldine, A., Harzallah, M., Berio, G., Béchet, N., Faour, A. (2020). DHPs: Dependency Hearst’s Patterns for Hypernym Relation Extraction. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2018. Communications in Computer and Information Science, vol 1222. Springer, Cham. https://doi.org/10.1007/978-3-030-49559-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-49559-6_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49558-9

  • Online ISBN: 978-3-030-49559-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics