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Comparing and combining semantic verb classifications

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Abstract

In this article, we address the task of comparing and combining different semantic verb classifications within one language. We present a methodology for the manual analysis of individual resources on the level of semantic features. The resulting representations can be aligned across resources, and allow a contrastive analysis of these resources. In a case study on the Manner of Motion domain across four German verb classifications, we find that some features are used in all resources, while others reflect individual emphases on specific meaning aspects. We also provide evidence that feature representations can ultimately provide the basis for linking verb classes themselves across resources, which allows us to combine their coverage and descriptive detail.

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Notes

  1. In the context of this article, we will use the more general terms verb classifications and verb classes to refer specifically to semantic verb classifications.

  2. We view features as describing prototypical rather than necessary and sufficient properties (Taylor 1989; Hampton 1993). See the discussion in Sect. 3 for details.

  3. While a feature gn.medium_fluid might have been preferable, given that swimming can take place in other fluids besides water, the feature fn.medium_water describes the prototypical case of swimming.

  4. Compare the discussion of the FrameNet relations in FrameNet in Sect. 2.

  5. In this section, we use “feature” to refer to resource-independent features which are linked as described in the previous section.

References

  • Atkins, S. (1992). Tools for corpus-aided lexicography: The HECTOR project. Acta Linguistica Hungarica, 41, 5–72.

    Google Scholar 

  • Ballmer, T. T., & Brennenstuhl, W. (1986). Deutsche Verben. Eine sprachanalytische Untersuchung des Deutschen Verbwortschatzes. Tübingen, Germany: Gunter Narr Verlag.

    Google Scholar 

  • Baroni, M., & Lenci, A. (2008). Concepts and properties in word spaces. Italian Journal of Linguistics. To appear.

  • Brants, S., Dipper, S., Hansen, S., Lezius, W., & Smith, G. (2002). The TIGER Treebank. In Proceedings of the Workshop on Treebanks and Linguistic Theories (pp. 24–41). Sozopol.

  • Budanitsky, A., & Hirst, G. (2006). Evaluating WordNet-based measures of lexical semantic relatedness. Computational Linguistics, 32(1), 13–47.

    Article  Google Scholar 

  • Burke, R. B., Hammond, K. J., & Kulyukin, V. A. (1997). Question answering from frequently-asked question files: Experiences with the FAQ finder system. AI Magazine, 18(2): 57(2), 57–66.

    Google Scholar 

  • Chapman, R. (Ed.) (1977). Roget’s international thesaurus (4th ed.). New York: Harper and Row.

    Google Scholar 

  • Chow, I., & Webster, J. (2007). Integration of linguistic resources for verb classification: FrameNet Frame, WordNet verb and suggested upper merged ontology. In Proceedings of the Eighth International Conference on Intelligent Text Processing and Computational Linguistics (pp. 1–11). Mexico City.

  • Cimiano, P. (2006). Ontology learning and population from text. Chapt. Learning attributes and relations. Springer.

  • Curran, J. (2004). From distributional to semantic similarity. Ph.D. thesis, University of Edinburgh. URL: http://hdl.handle.net/1842/56.

  • Dagan, I., Lee, L., & Pereira, F. (1999). Similarity-based models of word cooccurrence probabilities. Machine Learning, 34(13), 43–69. Special Issue on Natural Language Learning.

    Article  Google Scholar 

  • Dorr, B. J. (1997). Large-scale dictionary construction for foreign language tutoring and interlingual machine translation. Machine Translation, 12(4), 271–322.

    Article  Google Scholar 

  • Dorr, B. J., & Jones, D. (1996). Role of word sense disambiguation in lexical acquisition: Predicting semantics from syntactic cues. In Proceedings of the 16th International Conference on Computational Linguistics (pp. 322–327). Copenhagen, Denmark.

  • Ellsworth, M., Erk, K., Kingsbury, P., & Padó, S. (2004). PropBank, SALSA, and FrameNet: How design determines product. In Proceedings of the LREC Workshop on Building Lexical Resources From Semantically Annotated Corpora. Lisbon, Portugal.

  • Erk, K., Kowalski, A., Padó, S., & Pinkal, M. (2003). Towards a resource for lexical semantics: A large German corpus with extensive semantic annotation. In Proceedings of the 41st Annual Metting of the Association for Computational Linguistics (pp. 537–544). Sapporo, Japan.

  • Fellbaum, C. (Ed.) (1998). WordNet – An electronic lexical database, language, speech, and communication. Cambridge, MA: MIT Press.

    Google Scholar 

  • Fillmore, C. J. (1982). Frame semantics. In Linguistics in the morning calm (pp. 111–137). Seoul: Hanshin Publishing Co.

  • Fillmore, C. J., Johnson, C. R., & Petruck, M. R. (2003). Background to FrameNet. International Journal of Lexicography, 16(3), 235–250.

    Article  Google Scholar 

  • Giuglea, A.-M., & Moschitti, A. (2006). Semantic role labeling via FrameNet, VerbNet and PropBank. In Proceedings of the Joint Annual Meeting of the Association for Computational Linguistics and International Conference on Computational Linguistics (pp. 929–936). Sydney, Australia.

  • Gruber, J. S. (1965). Studies in lexical relations. Ph.D. thesis, Massachusetts Institute of Technology.

  • Hamp, B., & Feldweg, H. (1997). 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 (pp. 9–15). Madrid, Spain.

  • Hampton, J. (1993). Prototype models of concept representation. In I. V. Mechelen, J. Hampton, R. S. Michalski, & P. Theuns (Eds.), Categories and concepts: Theoretical views and inductive data analysis (pp. 1–2). London: Academic Press.

    Google Scholar 

  • Hearst, M. (1992). Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th International Conference on Computational Linguistics (pp. 539–545). Nantes, France.

  • Hovy, E., Marcus, M., Palmer, M., Pradhan, S., Ramshaw, L., Weischedel, R. (2006). OntoNotes: the 90% solution. In Proceedings of the Joint Human Language Technology and North American Association of Computational Linguistics Conference (pp. 57–60). New York.

  • Joanis, E., Stevenson, S., & James, D. (2008). A general feature space for automatic verb classification. Natural Language Engineering, 14(3), 337–367.

    Article  Google Scholar 

  • Katz, J. J., & Fodor, J. A. (1964). The structure of a semantic theory. In J. J. Katz & J. A. Fodor (Eds.), The structure of language. New York: Prentice-Hall.

  • Kipper, K., Dang, H. T., & Palmer, M. (2000). Class-based construction of a verb lexicon. In Proceeedings of the Seventeenth National Conference on Artificial Intelligence (pp. 691–696). Austin, TX.

  • Koehn, P., & Hoang, H. (2007). Factored translation models. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 868–876). Czech Republic: Prague.

  • Koenig, J. P., & Davis, A. R. (2001). Sublexical modality and the structure of lexical semantics. Linguistics and Philosophy, 24, 71–124.

    Article  Google Scholar 

  • Kohomban, U. S., & Lee, W. S. (2005). Learning semantic classes for word sense disambiguation. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 34–41). Ann Arbor, MI.

  • Korhonen, A. (2002). Subcategorization acquisition. Ph.D. thesis, University of Cambridge, Computer Laboratory. Technical Report UCAM-CL-TR-530.

  • Korhonen, A., Krymolowski, Y., & Marx, Z. (2003). Clustering polysemic subcategorization frame distributions semantically. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (pp. 64–71). Sapporo, Japan.

  • Kunze, C. (2000). Extension and use of GermaNet, a lexical-semantic database. In Proceedings of the 2nd International Conference on Language Resources and Evaluation (pp. 999–1002). Athens, Greece.

  • Levin, B. (1993). English verb classes and alternations. Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Lin, D. (1998). Automatic retrieval and clustering of similar words. In Proceedings of the 17th International Conference on Computational Linguistics (pp. 768–774). Montreal, Canada.

  • Maedche, A., & Staab, S. (2000). Discovering conceptual relations from text. In Proceedings of the 14th European Conference on Artificial Intelligence (pp. 321–325). Berlin, Germany.

  • McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C. (2005). Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods, 37, 547–559.

    Google Scholar 

  • Merlo, P., & Stevenson, S. (2001). Automatic verb classification based on statistical distributions of argument structure. Computational Linguistics, 27(3), 373–408.

    Article  Google Scholar 

  • Miller, G. A. (Ed.) (1990). WordNet: An on-line lexical database. Oxford University Press. Special issue of the International Journal of Lexicography, 3(4).

  • Nastase, V., Sayyad-Shiarabad, J., Sokolova, M., Szpakowic, S. (2006). Learning noun-modifier semantic relations with corpus-based and WordNet-based features. In Proceedings of the 21st National Conference on Artificial Intelligence (pp. 781–787). Boston, MA.

  • Navigli, R., & Velardi, P. (2003). An analysis of ontology-based query expansion strategies. In Proceedings of the ECML Workshop on Adaptive Text Extraction and Mining (pp. 42–49). Cavtat-Dubrovnik, Croatia.

  • Ohara, K. H. (2004). Manner of motion in English and Japanese. In Proceedings of the 3rd International Conference on Construction Grammar. Marseille, France.

  • Padó, U., Crocker, M., & Keller, F. (2006). Modelling semantic role plausibility in human sentence processing. In Proceedings of the 17th Meeting of the European Chapter of the Association for Computational Linguistics (pp. 345–352). Trento, Italy.

  • Padó, S., & Lapata, M. (2007). Dependency-based construction of semantic space models. Computational Linguistics, 33(2), 161–199.

    Article  Google Scholar 

  • Pantel, P., & Pennacchiotti, M. (2006). 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 (pp. 113–120). Sydney, Australia.

  • Prescher, D., Riezler, S., & Rooth, M. (2000). Using a probabilistic class-based lexicon for lexical ambiguity resolution. In Proceedings of the 18th International Conference on Computational Linguistics (pp. 649–655). Saarbrücken, Germany.

  • Schulte im Walde, S. (2003). Experiments on the automatic induction of German semantic verb classes. Ph.D. thesis, Institut für Maschinelle Sprachverarbeitung, Universität Stuttgart. Chapter 2.

  • Schulte im Walde, S. (2006). Experiments on the automatic induction of German semantic verb classes. Computational Linguistics, 32(2), 159–194.

    Article  Google Scholar 

  • Schulte im Walde, S., & Erk, K. (2005). A comparison of German semantic verb classifications. In Proceedings of the Sixth International Workshop on Computational Semantics. Tilburg, Netherlands.

  • Shen, D., & Lapata, M. (2007). Using semantic roles to improve question answering. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 12–21).

  • Shi, L., & Mihalcea, R. (2005). Putting pieces together: Combining FrameNet, VerbNet and WordNet for robust semantic parsing. In Proceedings of the 6th International Conference on Intelligent Text Processing and Computational Linguistics (pp. 100–111). Mexico City, Mexico.

  • Sloman, S. A., Love, B., & Ahn, W. (1998). Feature centrality and conceptual coherence. Cognitive Science, 22, 189–228.

    Google Scholar 

  • Taylor, J. R. (1989). Linguistic categorization. Oxford University Press.

  • Vigliocco, G., Vinson, D. P., Lewis, W., & Garrett, M. F. (2004). Representing the meanings of object and action words: The featural and unitary semantic space hypothesis. Cognitive Psychology, 48, 422–488.

    Article  Google Scholar 

  • Weeds, J., & Weir, D. (2005). A flexible framework for lexical distributional similarity. Computational Linguistics, 31(4), 439–476.

    Article  Google Scholar 

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Acknowledgements

The studies reported in this article were performed while the authors worked at Saarland University, Saarbrücken, Germany. We acknowledge the financial support of DFG (grants Pi-154/9-2 and IGK “Language technology and cognitive systems”).

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Authors and Affiliations

  1. Institute of Applied Linguistics, University of Mainz, Germersheim, Germany

    Oliver Čulo

  2. Department of Linguistics, University of Texas at Austin, Austin, TX, USA

    Katrin Erk

  3. Department of Linguistics, Stanford University, Stanford, CA, USA

    Sebastian Padó

  4. Institute for Natural Language Processing, University of Stuttgart, Stuttgart, Germany

    Sabine Schulte im Walde

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  1. Oliver Čulo
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  4. Sabine Schulte im Walde
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Correspondence to Sebastian Padó.

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Čulo, O., Erk, K., Padó, S. et al. Comparing and combining semantic verb classifications. Lang Resources & Evaluation 42, 265–291 (2008). https://doi.org/10.1007/s10579-008-9070-z

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