Learning to Interpret Novel Noun-Noun Compounds: Evidence from Category Learning Experiments

  • Barry J. DevereuxEmail author
  • Fintan J. Costello
Part of the Theory and Applications of Natural Language Processing book series (NLP)


The ability to correctly learn to interpret and produce novel noun-noun compounds such as wind farm or carbon tax is an important part of the acquisition of language in various domains of discourse. One approach to the interpretation of noun-noun compounds assumes that people make use of distributional information about the linguistic behaviour of words and how they tend to combine in noun-noun phrases; another assumes that people activate and integrate information about the two constituent concepts’ features to produce interpretations. We present a series of experiments that examine how people acquire both the distributional information and conceptual information that is relevant to compound interpretation. We propose that the relations used to link the two words in noun-noun compounds have rich semantic structure, which includes information about what features of concepts are necessary and/or characteristic for particular relations, as well as distributional information about the frequency with which relations co-occur with different concepts. We present an exemplar-based model of the semantics of relations which captures both of these aspects of relation meaning, and show how it can predict experimental participants’ interpretations of novel noun-noun compounds.


Relation Selection Head Noun Relation Likelihood Training Item Plant Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was funded by Irish Research Council for Science, Engineering and Technology Grant RS/2002/758-2 to BD.


  1. 1.
    Clark, E. V., & Barron, B. J. (1988). A thrower-button or a button-thrower? Children’s judgments of grammatical and ungrammatical compound nouns. Linguistics, 26, 3–19.CrossRefGoogle Scholar
  2. 2.
    Cannon, G. H. (1987). Historical change and English word formation. New York: Lang.Google Scholar
  3. 3.
    Devereux, B., & Costello, F. J. (2006). Modelling the interpretation and interpretation ease of noun-noun compounds using a relation space approach to compound meaning. In R. Sun & N. Miyake (Eds.), Proceedings of the Twenty-Eigth Annual Conference of the Cognitive Science Society (pp. 184–189). Mahwah: Cognitive Science Society/Lawrence Erlbaum. ISBN 0-9768318-2-1.Google Scholar
  4. 4.
    Levi, J. N. (1978). The syntax and semantics of complex nominals. New York: Academic.Google Scholar
  5. 5.
    Downing, P. (1977). On the creation and use of English compound nouns. Language, 53(4), 810–842.CrossRefGoogle Scholar
  6. 6.
    Gagné, C. L., & Shoben, E. J. (1997). Influence of thematic relations on the comprehension of modifier-noun combinations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 71–78.CrossRefGoogle Scholar
  7. 7.
    Kim, S. N., & Baldwin, T. (2005). Automatic interpretation of noun compounds using WordNet similarity. In Proceedings of the 2nd International Joint Conference on Natural Language Processing. Sydney: Association for Computational Linguistics.Google Scholar
  8. 8.
    Barr, R. A., & Caplan, L. J. (1987). Category representations and their implications for category structure. Memory and Cognition, 15, 397–418.CrossRefGoogle Scholar
  9. 9.
    Anggoro, F., Gentner, D., & Klibanoff, R. (2005). How to go from nest to home: Children’s learning of relational categories. In B. G. Bara, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society (pp. 133–138). Mahwah: Cognitive Science Society/Lawrence Erlbaum. ISBN 0-9768318-1-3.Google Scholar
  10. 10.
    Wisniewski, E. J. (1997). When concepts combine. Psychonomic Bulletin & Review, 4, 167–183.CrossRefGoogle Scholar
  11. 11.
    Costello, F. J. & Keane, M. T. (2000). Efficient creativity: Constraint-guided conceptual combination. Cognitive Science, 24(2), 299–349.CrossRefGoogle Scholar
  12. 12.
    Wisniewski, E. J., & Murphy, G. L. (2005). Frequency of relation type as a determinant of conceptual combination: A reanalysis. Journal of Experimental Psychology: Learning Memory and Cognition, 31, 169–174.CrossRefGoogle Scholar
  13. 13.
    Gagné, C. L., & Spalding, T. L. (2006). Relation availability was not confounded with familiarity or plausibility in gagné and shoben (1997): Comment on wisniewski and murphy (2005). Journal of experimental psychology. Learning, memory, and cognition, 32(6), 1431–1437; discussion 1438–1442. ISSN 02787393.Google Scholar
  14. 14.
    Tyler, L. K., Moss, H. E., Durrant-Peatfield, M. R., & Levy, J. P. (2000). Conceptual structure and the structure of concepts: A distributed account of category-specific deficits. Brain and Language, 75(2), 195–231.CrossRefGoogle Scholar
  15. 15.
    Tyler, L. K., & Moss, H. E. (2001). Towards a distributed account of conceptual knowledge. Trends in Cognitive Sciences, 5(6), 244–252.CrossRefGoogle Scholar
  16. 16.
    Pexman, P. M., Lupker, S. J., & Hino, Y. (2002). The impact of feedback semantics in visual word recognition: Number-of-features effects in lexical decision and naming tasks. Psychonomic Bulletin & Review, 9, 542–549.CrossRefGoogle Scholar
  17. 17.
    Pexman, P. M., Holyk, G. G., & Monfils, M. -H. (2003). Number-of-features effects and semantic processing. Memory & Cognition, 31, 842–855.CrossRefGoogle Scholar
  18. 18.
    Kounios, J., Green, D. L., Payne, L., Fleck, J. I., Grondin, R., & McRae, K. (2009). Semantic richness and the activation of concepts in semantic memory: Evidence from event-related potentials. Brain Research, 1282, 95–102. ISSN 1872-6240.Google Scholar
  19. 19.
    Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.CrossRefGoogle Scholar
  20. 20.
    Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104–114.CrossRefGoogle Scholar
  21. 21.
    Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.CrossRefGoogle Scholar
  22. 22.
    Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87–108.CrossRefGoogle Scholar
  23. 23.
    Gentner, D. (1981). Verb semantic structures in memory for sentences: Evidence for componential representation. Cognitive Psychology, 13, 56–83.CrossRefGoogle Scholar
  24. 24.
    Duncker, K. (1945). On problem solving (L. S. Lees, Trans.). Psychological Monographs, 58(5, Whole No. 270), 1–113.Google Scholar
  25. 25.
    Keane, M. T. (1985). Ton drawing analogies when solving problems. British Journal of Psychology, 76, 449–458.CrossRefGoogle Scholar
  26. 26.
    Costello, F. J. (2000). An exemplar model of classification in simple and combined categories. In L. R. Gleitman & J. K. Joshi (Eds.), Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society. Mahwah: Lawrence Erlbaum Associates.Google Scholar
  27. 27.
    Smith, J. D., & Minda, J. P. (2000). Thirty categorization results in search of a model. Journal Of Experimental Psychology: Learning Memory And Cognition, 26, 3–27.CrossRefGoogle Scholar
  28. 28.
    Costello, F. J. (2001). A computational model of categorisation and category combination: Identifying diseases and new disease combinations. In J. D. Moore & K. Stenning, (Eds.), Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, University of Edinburgh (pp. 238–243). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  29. 29.
    Nosofsky, R. M., & Zaki, S. R. (2002). Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 924–940.CrossRefGoogle Scholar
  30. 30.
    Zaki, S. R., Nosofsky, R. M., Stanton, R. D., & Cohen, A. L. (2003). Prototype and exemplar accounts of category learning and attentional allocation: A reassessment. Learning and Memory, 29, 1160–1173.CrossRefGoogle Scholar
  31. 31.
    Yamauchi, T., & Markman, A. B. (2000). Learning categories composed of varying instances: The effect of classification, inference, and structural alignment. Memory & Cognition, 28 64–78.CrossRefGoogle Scholar
  32. 32.
    Yamauchi, T., & Yu, N.-Y. (2008). Category labels versus feature labels: Category labels polarize inferential predictions. Memory & Cognition, 36, 544–553.CrossRefGoogle Scholar
  33. 33.
    Daelemans, W., Zavrel, J., van der Sloot, K., & van den Bosch, A. (2004). TiMBL: Tilburg memory based learner, Version 5.1, Reference guide, Tilburg University (ILK Techn. Rep. 04–02).Google Scholar
  34. 34.
    Shepard, R. N. (1987). Towards a universal law of generalization for psychological science. Science, 237, 1317–1323.MathSciNetzbMATHCrossRefGoogle Scholar
  35. 35.
    Nosofsky, R. M. (1985). Overall similarity and the identifcation of separable-dimension stimuli: A choice model analysis. Perception and Psychophysics, 38, 415–432.CrossRefGoogle Scholar
  36. 36.
    Nosofsky, R. M. (1988). Exemplar-based accounts of relations between classiffication, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700–708.CrossRefGoogle Scholar
  37. 37.
    Keselman, H. J. (1998). Testing treatment effects in repeated measures designs: An update for psychophysiological researchers. Psychophysiology, 35, 470–478.CrossRefGoogle Scholar
  38. 38.
    Boik, R. J. (1987). The analysis of two-factor interactions in fixed effects linear models. Journal of Educational Statistics, 18, 1–40.CrossRefGoogle Scholar
  39. 39.
    Devereux, B., & Costello, F. J. (2005). Representing and modelling the meaning of noun-noun compounds. In K. Opwis & I. Penner (Eds.), Proceedings of KogWis05: the Seventh Biannual Meeting of the German Cognitive Science Society (pp. 33–38). Basel: German Cognitive Science Society/Schwabe. ISBN ISBN 0-9768318-1-3.Google Scholar
  40. 40.
    Devereux, B. (2007). The Role of Relational and Conceptual Knowledge in the Interpretation of Noun-Noun Compounds. Ph.D. thesis, University College Dublin.Google Scholar
  41. 41.
    Seco, N., Veale, T., & Hayes, J. (2004). An intrinsic information content metric for semantic similarity in wordnet. In Proceedings of ECAI 2004, the 16th European Conference on Artificial Intelligence, (pp. 1089–1090). Valencia: IOS Press.Google Scholar
  42. 42.
    Gagné, C. L., & Spalding, T. L. (2009). Constituent integration during the processing of compound words: Does it involve the use of relational structures? Journal of Memory and Language, 60, 20–35.CrossRefGoogle Scholar
  43. 43.
    Estes, Z., & Jones, L. L. (2006). Priming via relational similarity: A copper horse is faster when seen through a glass eye. Journal of Memory and Language, 55, 89–101.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
  2. 2.School of Computer Science and InformaticsUniversity College DublinDublinIreland

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