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Language Processing and Knowledge in the Web pp 184–198Cite as

Pattern-Based Distinction of Paradigmatic Relations for German Nouns, Verbs, Adjectives

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8105))

Abstract

This paper implements a simple vector space model relying on lexico-syntactic patterns to distinguish between the paradigmatic relations synonymy, antonymy and hypernymy. Our study is performed across word classes, and models the lexical relations between German nouns, verbs and adjectives. Applying nearest-centroid classification to the relation vectors, we achieve a precision of 59.80%, which significantly outperforms the majority baseline (χ 2, p<0.05). The best results rely on large-scale, noisy patterns, without significant improvements from various pattern generalisations and reliability filters. Analysing the classification shows that (i) antonym/synonym distinction is performed significantly better than synonym/hypernym distinction, and (ii) that paradigmatic relations between verbs are more difficult to predict than paradigmatic relations between nouns or adjectives.

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References

  1. Murphy, M.L.: Semantic Relations and the Lexicon. Cambridge University Press (2003)

    Google Scholar 

  2. Edmonds, P., Hirst, G.: Near-Synonymy and Lexical Choice. Computational Linguistics 28(2), 105–144 (2002)

    Article  Google Scholar 

  3. Curran, J.: From Distributional to Semantic Similarity. PhD thesis, Institute for Communicating and Collaborative Systems, School of Informatics. University of Edinburgh (2003)

    Google Scholar 

  4. van der Plas, L., Tiedemann, J.: Finding Synonyms using Automatic Word Alignment and Measures of Distributional Similarity. In: Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, Sydney, Australia, pp. 866–873 (2006)

    Google Scholar 

  5. Fellbaum, C.: Co-Occurrence and Antonymy. Lexicography 8(4), 281–303 (1995)

    Article  Google Scholar 

  6. Harabagiu, S.M., Hickl, A., Lacatusu, F.: Negation, Contrast and Contradiction in Text Processing. In: Proceedings of the 21st National Conference on Artificial Intelligence, Boston, MA, pp. 755–762 (2006)

    Google Scholar 

  7. Mohammad, S., Dorr, B., Hirst, G.: Computing Word-Pair Antonymy. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Waikiki, Hawaii, pp. 982–991 (2008)

    Google Scholar 

  8. Hearst, M.: Automatic Acquisition of Hyponyms from Large Text Corpora. In: Proceedings of the 14th International Conference on Computational Linguistics, Nantes, France, pp. 539–545 (1992)

    Google Scholar 

  9. Caraballo, S.A.: Automatic Acquisition of a Hypernym-labeled Noun Hierarchy from Text. PhD thesis, Brown University (2001)

    Google Scholar 

  10. Snow, R., Jurafsky, D., Ng, A.Y.: Learning Syntactic Patterns for Automatic Hypernym Discovery. Advances in Neural Information Processing Systems 17, 1297–1304 (2004)

    Google Scholar 

  11. Turney, P.D.: A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations. In: Proceedings of the 22nd International Conference on Computational Linguistics, Manchester, UK, pp. 905–912 (2008)

    Google Scholar 

  12. Yih, W.T., Zweig, G., Platt, J.C.: Polarity Inducing Latent Semantic Analysis. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea, pp. 1212–1222 (2012)

    Google Scholar 

  13. Mohammad, S.M., Dorr, B.J., Hirst, G., Turney, P.D.: Computing Lexical Contrast. Computational Linguistics 39(3) (to appear, 2013)

    Google Scholar 

  14. Berland, M., Charniak, E.: Finding Parts in Very Large Corpora. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, Maryland, MD, pp. 57–64 (1999)

    Google Scholar 

  15. Girju, R., Badulescu, A., Moldovan, D.: Automatic Discovery of Part-Whole Relations. Computational Linguistics 32(1), 83–135 (2006)

    Google Scholar 

  16. Girju, R.: Automatic Detection of Causal Relations for Question Answering. In: Proceedings of the ACL Workshop on Multilingual Summarization and Question Answering – Machine Learning and Beyond, Sapporo, Japan, pp. 76–83 (2003)

    Google Scholar 

  17. Chklovski, T., Pantel, P.: VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain, pp. 33–40 (2004)

    Google Scholar 

  18. Pantel, P., Pennacchiotti, M.: Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, pp. 113–120 (2006)

    Google Scholar 

  19. Turney, P.D.: Similarity of Semantic Relations. Computational Linguistics 32(3), 379–416 (2006)

    Article  MATH  Google Scholar 

  20. Edmonds, P.: Choosing the Word most typical in Context using a Lexical Co-occurrence Netword. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, Madrid, Spain, pp. 507–509 (1997)

    Google Scholar 

  21. Edmonds, P.: Translating Near-Synonyms: Possibilities and Preferences in the Interlingua. In: Proceedings of the AMTA/SIG-IL Second Workshop on Interlinguas, Langhorne, PA, pp. 23–30 (1998)

    Google Scholar 

  22. Edmonds, P.: Semantic Representations of Near-Synonyms for Automatic Lexical Choice. PhD thesis, Department of Computer Science. University of Toronto, Published as technical report CSRI-399 (1999)

    Google Scholar 

  23. Curran, J.: Ensemble Methods for Automatic Thesaurus Extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 222–229 (2002)

    Google Scholar 

  24. Lin, D., Zhao, S., Qin, L., Zhou, M.: Identifying Synonyms among Distributionally Similar Words. In: Proceedings of the International Conferences on Artificial Intelligence, Acapulco, Mexico, pp. 1492–1493 (2003)

    Google Scholar 

  25. Erk, K., Padó, S.: A Structured Vector Space Model for Word Meaning in Context. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Waikiki, Hawaii, pp. 897–906 (2008)

    Google Scholar 

  26. Weeds, J., Weir, D., McCarthy, D.: Characterising Measures of Lexical Distributional Similarity. In: Proceedings of the 20th International Conference of Computational Linguistics, Geneva, Switzerland, pp. 1015–1021 (2004)

    Google Scholar 

  27. Lenci, A., Benotto, G.: Identifying Hypernyms in Distributional Semantic Spaces. In: Proceedings of the 1st Joint Conference on Lexical and Computational Semantics, Montréal, Canada, pp. 75–79 (2012)

    Google Scholar 

  28. Caraballo, S.A.: Automatic Construction of a Hypernym-labeled Noun Hierarchy from Text. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, Maryland, MD, pp. 120–126 (1999)

    Google Scholar 

  29. Velardi, P., Fabriani, P., Missikoff, M.: Using Text Processing Techniques to Automatically enrich a Domain Ontology. In: Proceedings of the International Conference on Formal Ontology in Information Systems, Ogunquit, ME, pp. 270–284 (2001)

    Google Scholar 

  30. Cimiano, P., Schmidt-Thieme, L., Pivk, A., Staab, S.: Learning Taxonomic Relations from Heterogeneous Evidence. In: Proceedings of the ECAI Workshop on Ontology Learning and Population (2004)

    Google Scholar 

  31. Snow, R., Jurafsky, D., Ng, A.Y.: Semantic Taxonomy Induction from Heterogenous Evidence. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, pp. 801–808 (2006)

    Google Scholar 

  32. Fellbaum, C.: English Verbs as a Semantic Net. Journal of Lexicography 3(4), 278–301 (1990)

    Article  Google Scholar 

  33. Fellbaum, C., Chaffin, R.: Some Principles of the Organization of Verbs in the Mental Lexicon. In: Proceedings of the 12th Annual Conference of the Cognitive Science Society of America, pp. 420–427 (1990)

    Google Scholar 

  34. Fellbaum, C.: A Semantic Network of English Verbs. In: [46], pp. 69–104

    Google Scholar 

  35. Charles, W., Miller, G.: Contexts of Antonymous Adjectives. Applied Psycholinguistics 10, 357–375 (1989)

    Article  Google Scholar 

  36. Justeson, J.S., Katz, S.M.: Co-Occurrence of Antonymous Adjectives and their Contexts. Computational Linguistics 17, 1–19 (1991)

    Google Scholar 

  37. Lucerto, C., Pinto, D., Jiménez-Salazar, H.: An Automatic Method to Identify Antonymy Relations. In: Proceedings of the IBERAMIA Workshop on Lexical Resources and the Web for Word Sense Disambiguation, Puebla, Mexico, pp. 105–111 (2004)

    Google Scholar 

  38. de Marneffe, M.C., Rafferty, A.N., Manning, C.D.: Finding Contradictions in Text. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Columbus, OH, pp. 1039–1047 (2008)

    Google Scholar 

  39. Lin, D., Pantel, P.: DIRT – Discovery of Inference Rules from Text. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, San Francisco, CA, pp. 323–328 (2001)

    Google Scholar 

  40. Turney, P.D.: Measuring Semantic Similarity by Latent Relational Analysis. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, pp. 1136–1141 (2005)

    Google Scholar 

  41. Turney, P.D.: Expressing Implicit Semantic Relations without Supervision. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, pp. 313–320 (2006)

    Google Scholar 

  42. Lin, D.: Automatic Retrieval and Clustering of Similar Words. In: Proceedings of the 17th International Conference on Computational Linguistics, Montreal, Canada, pp. 768–774 (1998)

    Google Scholar 

  43. Hamp, B., Feldweg, H.: GermaNet – a Lexical-Semantic Net for German. In: Proceedings of the ACL Workshop on Automatic Information Extraction and Building Lexical Semantic Resources for NLP Applications, Madrid, Spain, pp. 9–15 (1997)

    Google Scholar 

  44. Kunze, C.: Extension and Use of GermaNet, a Lexical-Semantic Database. In: Proceedings of the 2nd International Conference on Language Resources and Evaluation, Athens, Greece, pp. 999–1002 (2000)

    Google Scholar 

  45. Lemnitzer, L., Kunze, C.: Computerlexikographie. Gunter Narr Verlag, Tübingen (2007)

    Google Scholar 

  46. Fellbaum, C. (ed.): WordNet – An Electronic Lexical Database. Language, Speech, and Communication. MIT Press, Cambridge (1998)

    Google Scholar 

  47. Faaß, G., Heid, U., Schmid, H.: Design and Application of a Gold Standard for Morphological Analysis: SMOR in Validation. In: Proceedings of the 7th International Conference on Language Resources and Evaluation, Valletta, Malta, pp. 803–810 (2010)

    Google Scholar 

  48. Baroni, M., Bernardini, S., Ferraresi, A., Zanchetta, E.: The WaCky Wide Web: A Collection of Very Large Linguistically Processed Web-Crawled Corpora. Language Resources and Evaluation 43(3), 209–226 (2009)

    Article  Google Scholar 

  49. Schiller, A., Teufel, S., Stöckert, C., Thielen, C.: Guidelines für das Tagging deutscher Textcorpora mit STTS. Institut für Maschinelle Sprachverarbeitung, Universität Stuttgart, and Seminar für Sprachwissenschaft, Universität Tübingen (1999)

    Google Scholar 

  50. Miller, G.A., Fellbaum, C.: Semantic Networks of English. Cognition 41, 197–229 (1991)

    Article  Google Scholar 

  51. Church, K.W., Hanks, P.: Word Association Norms, Mutual Information, and Lexicography. Computational Linguistics 16(1), 22–29 (1990)

    Google Scholar 

  52. Christopher, D., Manning, P.R., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institut für Maschinelle Sprachverarbeitung, Universität Stuttgart, Germany

    Sabine Schulte im Walde & Maximilian Köper

Authors
  1. Sabine Schulte im Walde
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  2. Maximilian Köper
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Editor information

Editors and Affiliations

  1. Technical University Darmstadt, 64289 Darmstadt, Germany, and German Institute for International Education Research,, 60486, Frankfurt, Germany

    Iryna Gurevych

  2. Technical University Darmstadt, 64289, Darmstadt, Germany

    Chris Biemann

  3. Technical University Darmstadt, 64289 Darmsadt, and German Institute for International Educational Research, 60486, Frankfurt, Germany

    Torsten Zesch

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Schulte im Walde, S., Köper, M. (2013). Pattern-Based Distinction of Paradigmatic Relations for German Nouns, Verbs, Adjectives. In: Gurevych, I., Biemann, C., Zesch, T. (eds) Language Processing and Knowledge in the Web. Lecture Notes in Computer Science(), vol 8105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40722-2_19

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  • DOI: https://doi.org/10.1007/978-3-642-40722-2_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40721-5

  • Online ISBN: 978-3-642-40722-2

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