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Within-concept similarities in a taxonomy: a corpus linguistic approach

Published online by Cambridge University Press:  13 June 2014

STIJN STORMS ,
DIRK SPEELMAN ,
DIRK GEERAERTS  and
GERT STORMS
STIJN STORMS*
Affiliation:
QLVL, KU Leuven, Belgium
DIRK SPEELMAN*
Affiliation:
QLVL, KU Leuven, Belgium
DIRK GEERAERTS*
Affiliation:
QLVL, KU Leuven, Belgium
GERT STORMS*
Affiliation:
Experimentele Psychologie, KU Leuven, Belgium
*
*Addresses for correspondence: Stijn Storms: [email protected]; Dirk Speelman: [email protected]; Dirk Geeraerts: [email protected]; Gert Storms: [email protected].
*Addresses for correspondence: Stijn Storms: [email protected]; Dirk Speelman: [email protected]; Dirk Geeraerts: [email protected]; Gert Storms: [email protected].
*Addresses for correspondence: Stijn Storms: [email protected]; Dirk Speelman: [email protected]; Dirk Geeraerts: [email protected]; Gert Storms: [email protected].
*Addresses for correspondence: Stijn Storms: [email protected]; Dirk Speelman: [email protected]; Dirk Geeraerts: [email protected]; Gert Storms: [email protected].
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Abstract

This paper looks at a hitherto unexplored aspect of taxonomically organized concepts which has to do with word distributions in corpora of actual language use. In parallel to the psychological informativeness claim of the differentiation explanation, the question is addressed if concepts are internally more similar than their higher-ranked taxonomical relatives. This internal similarity is measured by making use of token-based vector space models. For each occurrence of a concept in the corpus a context vector can be calculated, which then serves as input for the internal similarity measure. Experiments are conducted for taxonomies taken from the Dutch counterparts of the English semantic domains animal and means of transportation. Results do not wholeheartedly agree with the imposition of a strict taxonomical order, but give rise to a new behavioural measure of the basic level.

Keywords

basic levelcategorizationcorpus linguisticslexical semanticstaxonomy
Type
Research Article
Information
Language and Cognition , Volume 7 , Issue 2 , June 2015 , pp. 194 - 218
Copyright
Copyright © UK Cognitive Linguistics Association 2014 

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References

references

Agirre, E., & Edmonds, P. G. (2006). Word sense disambiguation: algorithms and applications (Text, Speech, and Language Technology). Dordrecht: Springer.CrossRefGoogle Scholar
Anglin, J. M. (1977). Word, object, and conceptual development. New York: Norton.Google Scholar
Barsalou, L. W. (1983). Ad hoc categories. Memory & Cognition, 11 (3), 211–227.CrossRefGoogle ScholarPubMed
Berlin, B., Breedlove, D. E., & Raven, P. H. (1973). General principles of classification and nomenclature in folk biology. American Anthropologist, 75 (1), 214–242.CrossRefGoogle Scholar
Boon, T. den, & Geeraerts, D. (2005). Van Dale Groot woordenboek van de Nederlandse taal. Utrecht/Antwerpen: Van Dale Lexicografie bv.Google Scholar
Bouma, G., Van Noord, G., & Malouf, Robert (2001). Alpino: wide-coverage computational analysis of Dutch. In Daelemans, W., Sima’an, K., Veenstra, J., & Zavrel, J. (Eds.), Computational Linguistics in the Netherlands 2000. Selected Papers from the 11th CLIN Meeting (pp. 45–59). Amsterdam: Rodopi.CrossRefGoogle Scholar
Cruse, D. A. (1977). The pragmatics of lexical specificity. Journal of Linguistics, 13 (2), 153–164.CrossRefGoogle Scholar
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41 (6), 391–407.3.0.CO;2-9>CrossRefGoogle Scholar
Erk, K. (2009). Representing words as regions in vector space. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (pp. 57–65). Association for Computational Linguistics, online: <http://aclweb.org/anthology//W/W09/W09-1109.pdf>.CrossRefGoogle Scholar
Erk, K., & Padó, S. (2010). Exemplar-based models for word meaning in context. In Proceedings of the ACL 2010 Conference short papers (pp. 92−97). Association for Computational Linguistics, online” <http://aclweb.org/anthology//P/P10/P10-2017.pdf>.Google Scholar
Firth, J. R. (1957). A synopsis of linguistic theory, 1930−1955. In Firth, J. R. (Ed.), Studies in linguistic analysis (pp. 1−32). Oxford: Blackwell.Google Scholar
Geeraerts, D., Grondelaers, S., & Bakema, P. (1994). The structure of lexical variation: meaning, naming and context. New York: M. de Gruyter.CrossRefGoogle Scholar
Harris, Z. S. (1954). Distributional structure. Word, 10, 146−162.CrossRefGoogle Scholar
Jolicoeur, P., Gluck, M. A., & Kosslyn, S. M. (1984). Pictures and names: making the connection. Cognitive Psychology, 16, 243−275.CrossRefGoogle ScholarPubMed
Lin, E. L., & Murphy, G. L. (1997). Effects of background knowledge on object categorization and part detection. Journal of Experimental Psychology: Human Perception and Performance, 23, 1153−1169.Google Scholar
Markman, A. B., & Wisniewski, E. J. (1997). Similar and different: the differentiation of basic-level categories. Journal of Experimental Psychology: Learning, Memory and Cognition, 23, 54−70.Google Scholar
Markman, E. M. (1985). Why superordinate category terms can be mass nouns. Cognition, 19, 31−53.CrossRefGoogle ScholarPubMed
Mervis, C. B., & Crisafi, M. A. (1982). Order of acquisition of subordinate-level, basic-level and superordinate-level categories. Child Development, 53, 258−266.CrossRefGoogle Scholar
Morris, M., & Murphy, G. L. (1990). Converging operations on a basic level in event taxonomies. Memory & Cognition, 18, 407−418.CrossRefGoogle ScholarPubMed
Murphy, G. L. (2002). The big book of concepts. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Murphy, G. L., & Brownell, H. H. (1985). Category differentiation in object recognition: typicality constraints on the basic category advantage. Journal of Experimental Psychology: Learning, Memory and Cognition, 11, 70−84.Google ScholarPubMed
Murphy, G. L., & Smith, E. E. (1982). Basic-level superiority in picture categorization. Journal of Verbal Learning and Verbal Behavior, 21, 1−20.CrossRefGoogle Scholar
Peirsman, Y. (2010). Crossing corpora: modelling semantic similarity across languages and lects. Unpublished doctoral dissertation, KU Leuven.Google Scholar
Reddy, S., Klapaftis, I. P., McCarthy, D., & Manandhar, S. (2011). Dynamic and static prototype vectors for semantic composition. In Proceedings of 5th International Joint Conference on Natural Language Processing (pp. 705−713), online: <http://aclweb.org/anthology//I/I11/I11-1079.pdf>.Google Scholar
Reisinger, J., & Mooney, R. J. (2010). Multi-prototype vector space models of word meaning. In Human Language Technologies: the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 109−117). Association for Computational Linguistics, online: <http://www.cs.utexas.edu/users/ml/papers/reisinger.naacl-2010.pdf>.Google Scholar
Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382−439.CrossRefGoogle Scholar
Sagi, E., Kaufmann, S., & Clark, B. (2009). Semantic density analysis: comparing word meaning across time and phonetic space. In Proceedings of the Workshop on Geometrical Models of Natural Language Semantics (pp. 104−111). Athens: Association for Computational Linguistics.CrossRefGoogle Scholar
Schütze, H. (1998). Automatic word sense discrimination. Computational Linguistics, 24, 97−123.Google Scholar
Tanaka, J., & Taylor, M. (1991). Object categories and expertise: Is the basic level in the eye of the beholder? Cognitive Psychology, 23, 457−482.CrossRefGoogle Scholar
Turney, P. D., & Pantel, P. (2010). From frequency to meaning: vector space models of semantics. Journal of Artificial Intelligence Research, 37, 141−188.CrossRefGoogle Scholar
Tversky, B., & Hemenway, K. (1983). Categories of environmental scenes. Cognitive Psychology, 15, 121−149.CrossRefGoogle Scholar
Tversky, B., & Hemenway, K. (1984). Objects, parts and categories. Journal of Experimental Psychology: General, 113, 169−193.CrossRefGoogle ScholarPubMed
Wisniewski, E. J., Imai, M., & Casey, L. (1996). On the equivalence of superordinate concepts. Cognition, 60, 269−298.CrossRefGoogle ScholarPubMed
Wisniewski, E. J., & Murphy, G. L. (1989). Superordinate and basic category names in discourse. Discourse Processes, 12, 245−261.CrossRefGoogle Scholar