Article published In: The Mental Lexicon
Vol. 11:1 (2016) ► pp.76–93
Transitivity in similarity judgments on German verbs
Disclosing lexical and aspectual properties of verbs
Published online: 16 June 2016
https://doi.org/10.1075/ml.11.1.04ric
https://doi.org/10.1075/ml.11.1.04ric
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.
References (34)
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.
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.
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.
Bybee, J., & Eddington, D. (2006). A usage-based approach to Spanish verbs of becoming. Language, 821, 323–354.
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.
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.
Fürstenau, H. (2011). Semi-supervised Semantic Role Labeling via Graph Alignment. Saarbrücken Dissertations in Computational Linguistics and Language Technology, Volume 321. German Research Center for Artificial Intelligence and Saarland University, Germany.
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.
Klavans, J.L., & Chodorow, M. (1992). Degrees of Stativity: The lexical representation of verb aspect.
Proceedings of the 14th International Conference on Computational Linguistics
.
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.
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. 31 (pp. 249–410). New York, NY: Wiley.
Miller, G.A., & Charles, W.G. (1991). Contextual correlates of semantic similarity. Cognitive Processes, 6(1), 1–28.
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, 431, 1–22.
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).
Regenwetter, M.J., Dana, J., & Davis-Stober, C. (2011). Transitivity of preferences. Psychological Review, 1181, 42–56.
Richter, M., & van Hout, R. (2010). Why some verbs can form a resultative construction While others cannot: Decomposing Semantic Binding. Lingua 120(8), 2006–2021.
. (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).
Rips, L.J. (2011). Split identity: Intransitive judgments of the identity of objects. Cognition, 1191, 356–373.
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.
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. 91, Nr. 2).
. (2004). Automatic induction of semantic classes for German verbs. In S. Langer & D. Schnorbusch (Eds.), Semantik im Lexikon (pp. 59–86). Tübingen: Narr.
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.
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.
. (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.
Schumacher, H. (1986). Verben in Feldern. Valenzwörterbuch zur Syntax und Semantik deutscher Verben. Berlin, New York: de Gruyter.
Shimodaira, H. (2004). Approximately unbiased tests of region using multistep-multiscale bootstrap resampling. The Annals of Statistics, 32(6), 2616–2641.
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
.
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.
Suzuki, R., & Shimodaira, H. (2006a). Pvclust: An R package for assessing the uncertainty in hierarchical clustering. Bioinformatics, 22(12), 1540–1542.
Tremblay, A. (2005). Theoretical and methodological perspectives on the use of grammaticality judgment tasks in linguistic theory. Second Language Studies, 241, 129–167.
Suzuki, R., & Shimodaira, H. (2006b). Pvclust. An R package for hierarchical clustering with p-values. [URL]