1887
Volume 11, Issue 1
  • ISSN 1871-1340
  • E-ISSN: 1871-1375
USD
Buy:$35.00 + Taxes

Abstract

This paper investigates set-theoretical transitive and intransitive similarity relationships in triplets of verbs that can be deduced from raters’ similarity judgments on the pairs of verbs involved. We collected similarity judgments on pairs made up of 35 German verbs and found that the concept of transitivity adds to the information obtained from collecting pair-wise semantic similarity judgments. The concept of transitive similarity enables more complex relations to be revealed in triplets of verbs. To evaluate the outcomes that we obtained by analyzing transitive similarities we used two previously developed verb classifications of the same set of 35 verbs based on the analysis of large corpora (Richter & van Hout, 2016). We applied a modified form of weak stochastic transitivity (Block & Marschak, 1960; Luce & Suppes, 1965; Tversky, 1969) and found that (1), in contrast to Rips’ claim (2011), similarity relations in raters’ judgments systematically turn out to be transitive, and (2) transitivity discloses lexical and aspectual properties of verbs relevant in distinguishing verb classes.

Loading

Article metrics loading...

/content/journals/10.1075/ml.11.1.04ric
2016-06-07
2019-09-19
Loading full text...

Full text loading...

References

  1. Belica, C
    (2011) Semantische Nähe als Ähnlichkeit von Kookkurrenzprofilen. In A. Abel & R. Zanin (Eds.), Korpora in Lehre und Forschung (pp.155–178). Bolzano: Bozen-Bolzano University Press.
    [Google Scholar]
  2. Block, H.D. , & Marschak, J
    (1960) Random orderings and stochastic theories of responses. In I. Olkin , S. Ghurye , H. Hoeffding , W. Madow , & H. Mann (Eds.), Contributions to probability and statistics (pp. 97–132). Stanford, CA: Stanford University Press.
    [Google Scholar]
  3. Burchardt, A. , Erk, K. , Frank, K.A. , Pado, S. , & Pinkal, M
    (2006) The SALSA Corpus: A German corpus resource for lexical semantics. Proceedings of the 5th International language resources and evaluation conference (pp.969–974). Genoa.
    [Google Scholar]
  4. Bybee, J. , & Eddington, D
    (2006) A usage-based approach to Spanish verbs of becoming. Language, 82, 323–354. doi: 10.1353/lan.2006.0081
    https://doi.org/10.1353/lan.2006.0081 [Google Scholar]
  5. Diaz, S. , Garcia-Lapresta, J. , & Montes, S
    (2008) Consistent models of transitivity for reciprocal preferences on a finite ordinal scale. Information Sciences, 178, 13, 2832–2848. doi: 10.1016/j.ins.2008.02.013
    https://doi.org/10.1016/j.ins.2008.02.013 [Google Scholar]
  6. Fanselow, G. , & Frisch, S
    (2006) Effects of processing difficulty on judgments of acceptability. In G. Fanselow , C. Fery , M. Schlesewsky , & R. Vogel (Eds.), Gradience in grammar (pp. 291–316). Oxford, UK: Oxford University Press. doi: 10.1093/acprof:oso/9780199274796.003.0015
    https://doi.org/10.1093/acprof:oso/9780199274796.003.0015 [Google Scholar]
  7. Fürstenau, H
    (2011) Semi-supervised Semantic Role Labeling via Graph Alignment. Saarbrücken Dissertations in Computational Linguistics and Language Technology, Volume 32. German Research Center for Artificial Intelligence and Saarland University, Germany.
    [Google Scholar]
  8. Hirschfeld, G. , Bien, H. , de Vries, M. , Lüttmann, H. , & Schwall, J
    (2010) OR-VIS: Open-source software to conduct online rating studies. Behavior Research Methods. 42, 2, 542–546. doi: 10.3758/BRM.42.2.542
    https://doi.org/10.3758/BRM.42.2.542 [Google Scholar]
  9. Klavans, J.L. , & Chodorow, M
    (1992) Degrees of Stativity: The lexical representation of verb aspect. Proceedings of the 14th International Conference on Computational Linguistics .
    [Google Scholar]
  10. Kupietz, M. , & Keibel, H
    (2009) Gebrauchsbasierte Grammatik: Statistische Regelhaftigkeit. In M. Konopka & B. Strecker (Eds.), Deutsche Grammatik – Regeln, Normen, Sprachgebrauch (pp. 33–50). Berlin & New York: de Gruyter.
    [Google Scholar]
  11. Levin, B
    (1993) English verb classes and alternations. Chicago: The University of Chicago Press.
    [Google Scholar]
  12. Luce, R.D. , & Suppes, P
    (1965) Preference, utility and subjective probability. In R.D. Luce , R.R. Bush , & E. Galanter (Eds.), Handbook of mathematical psychology, Vol. 3 (pp. 249–410). New York, NY: Wiley.
    [Google Scholar]
  13. Miller, G.A. , & Charles, W.G.
    (1991) Contextual correlates of semantic similarity. Cognitive Processes, 6(1), 1–28. doi: 10.1080/01690969108406936
    https://doi.org/10.1080/01690969108406936 [Google Scholar]
  14. Öztürk, P. , Vulchanova, M. , Martinez, L. , Tumyr, C. , & Kabath, D
    (2011) Assessing the feature-driven nature of similarity-based sorting of verbs. Polibits Research Journal on Computer Science and Computer Engeneering with Applications, 43, 1–22.
    [Google Scholar]
  15. Pahikkala, T. , Waegeman, W. , Tsivtsivadze, E. , De Baets, B.D. , & Salakoski, T
    (2009) From ranking to intransitive preference learning: Rock-Raper-Scissors and beyond. In E. Hüllermeier & J. Fürnkranz (Eds.), Proceedings of the ECML/PKDD-Workshop on Preference Learning (PL-09) (pp. 84–100).
    [Google Scholar]
  16. Regenwetter, M.J. , Dana, J. , & Davis-Stober, C
    (2011) Transitivity of preferences. Psychological Review, 118, 42–56. doi: org/10.1037/A0021150
    https://doi.org/org/10.1037/A0021150 [Google Scholar]
  17. Richter, M. , & van Hout, R
    (2010) Why some verbs can form a resultative construction While others cannot: Decomposing Semantic Binding. Lingua120(8), 2006–2021. doi: 10.1016/j.lingua.2010.02.007
    https://doi.org/10.1016/j.lingua.2010.02.007 [Google Scholar]
  18. (2016) A classification of German verbs using empirical data and conceptions of Vendler and Dowty. Sprache und Datenverarbeitung – International Journal for Language Data Processing, 38 (1:2).
    [Google Scholar]
  19. Rips, L.J.
    (2011) Split identity: Intransitive judgments of the identity of objects. Cognition, 119, 356–373. doi: 10.1016/j.cognition.2011.01.019
    https://doi.org/10.1016/j.cognition.2011.01.019 [Google Scholar]
  20. Schulte im Walde, S. , & Brew, C
    (2002) Inducing German semantic verb classes from purely syntactic subcategorisation information. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL) , 223–230. Philadelphia.
    [Google Scholar]
  21. Schulte im Walde, S
    (2003) Experiments on the automatic induction of German semantic verb classes. Dissertation Universität Stuttgart (=Arbeitspapiere des Instituts für Maschinelle Sprachverarbeitung Lehrstuhl für Theoretische Computerlinguistik, Vol. 9, Nr. 2).
    [Google Scholar]
  22. (2004) Automatic induction of semantic classes for German verbs. In S. Langer & D. Schnorbusch (Eds.), Semantik im Lexikon (pp. 59–86). Tübingen: Narr.
    [Google Scholar]
  23. Schulte im Walde, S. & Melinger, S.A.
    (2005) Identifying semantic relations and functional properties of human verb associations. Proceedings of the joint Conference on Human Language Technology and Empirical Methods in Natural Language Processing ; 612–619. Vancouver. doi: 10.3115/1220575.1220652
    https://doi.org/10.3115/1220575.1220652 [Google Scholar]
  24. Schulte im Walde, S
    (2006a) Human verb associations as the basis for Gold Standard verb classes: Validation against GermaNet and FrameNet. Proceedings of the 5th Conference on Language Resources and Evaluation , 825–830. Genoa.
    [Google Scholar]
  25. (2006b) Can human verb associations help identify salient features for semantic verb classification? Proceedings of the 10th Conference on Computational Natural Language Learning , 69–76. New York City.
    [Google Scholar]
  26. Schumacher, H
    (1986) Verben in Feldern. Valenzwörterbuch zur Syntax und Semantik deutscher Verben. Berlin, New York: de Gruyter. doi: 10.1515/9783110861853
    https://doi.org/10.1515/9783110861853 [Google Scholar]
  27. Shimodaira, H
    (2004) Approximately unbiased tests of region using multistep-multiscale bootstrap resampling. The Annals of Statistics, 32(6), 2616–2641. doi: 10.1214/009053604000000823
    https://doi.org/10.1214/009053604000000823 [Google Scholar]
  28. Siegel, E.V.
    (1997) Learning methods for combining linguistic indicators to classify verbs. Proceedings of the Second Conference on Empirical Methods in Natural Language Processing .
    [Google Scholar]
  29. Siegel, E.V. , & McKeown, K.R.
    (2000) Learning methods to combine linguistic indicators: Improving aspectual classification and revealing linguistic insights. Computational Linguistics, 26(4), 595–627. doi: 10.1162/089120100750105957
    https://doi.org/10.1162/089120100750105957 [Google Scholar]
  30. Suzuki, R. , & Shimodaira, H
    (2006a) Pvclust: An R package for assessing the uncertainty in hierarchical clustering. Bioinformatics, 22(12), 1540–1542. doi: 10.1093/bioinformatics/btl117
    https://doi.org/10.1093/bioinformatics/btl117 [Google Scholar]
  31. Tremblay, A
    (2005) Theoretical and methodological perspectives on the use of grammaticality judgment tasks in linguistic theory. Second Language Studies, 24, 129–167.
    [Google Scholar]
  32. Tversky, A
    (1969) Intransitivity of preferences. Psychological Review, 76, 31–48. doi: 10.1037/h0026750
    https://doi.org/10.1037/h0026750 [Google Scholar]
  33. Vendler, Z
    (1967) Linguistics in philosophy. Ithaka, NY: Cornell University Press.
    [Google Scholar]
  34. Suzuki, R. , & Shimodaira, H
    (2006b) Pvclust. An R package for hierarchical clustering with p-values. www.sigmath.es.osaka-u.ac.jp/shimo-lab/prog/pvclust/
http://instance.metastore.ingenta.com/content/journals/10.1075/ml.11.1.04ric
Loading
This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error