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Conceptual Combination with PUNC

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Abstract

Noun–noun compounds play a key role in the growth of language. In this article we present a system for producing and understanding noun–noun compounds (PUNC). PUNC is based on the Constraint theory of conceptual combination and the C 3 model. The new model incorporates the primary constraints of the Constraint theory in an integrated fashion, creating a cognitively plausible mechanism of interpreting noun–noun phrases. It also tries to overcome algorithmic limitations of the C 3 model in being more efficient in its computational complexity, and deal with a wider span of empirical phenomena, such as dimensions of word familiarity. We detail the model, including knowledge representation and interpretation production mechanisms. We show that by integrating the constraints of the Constraint theory of conceptual combination and prioritizing the knowledge available within a concept's representation, PUNC can not only generate interpretations that reflect those produced by people, but also mirror the differences in processing times for understanding familiar, similar and novel word combinations.

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References

  • Blum, A. & Langley, P. (1997). Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence 97: 245–271.

    Google Scholar 

  • Bock, J. B. and Clifton, C. Jr. (2000). The Role of Salience in Conceptual Combination.Memory and Cognition 28: 1378–1386.

    Google Scholar 

  • Clark, E. V. & Hecht, B. F. (1982). Learning to Coin Agent and Instrument Nouns.Cognition 12: 1–24.

    Google Scholar 

  • Cohen, B. & Murphy, G. L. (1984). Models of Concepts. Cognitive Science 8: 27–58.

    Google Scholar 

  • Costello, F. J. (2002). Investigating Creative Language: People's Choice of Words in the Production of Noun-Noun Compounds. In Gray, W. D. & Schunn, C. D. (eds.) Twenty-Fourth Annual Conference of the Cognitive Science Society, 232–237. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Costello, F. J. & Keane, M. T. (1997). Polysemy in Conceptual Combination: Testing the Constraint Theory of Combination. In Shafto, M. G. & Langley, P. (eds.) Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, 137–142. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Costello, F. J. & Keane, M. T. (2000). Efficient Creativity: Constraint-Guided Conceptual Combination. Cognitive Science 24: 299–349.

    Google Scholar 

  • Costello, F. J. & Keane, M. T. (2001). Testing Two Theories of Conceptual Combination: Alignment versus Diagnosticity in the Comprehension and Production of Combined Concepts. Journal of Experimental Psychology: Learning, Memory, and Cognition 27: 255–271.

    Google Scholar 

  • Coulson, S. (2001). Semantic Leaps: Frame Shifting and Conceptual Blending in Meaning Construction. New York and Cambridge: Cambridge University Press.

    Google Scholar 

  • Estes, Z. & Glucksberg, S. (2000). Interactive Property Attribution in Concept Combination.Memory and Cognition 28: 28–34.

    Google Scholar 

  • Forbus, K. D., Ferguson, R. W. & Gentner, D. (1994). Incremental Structure Mapping.In Ram, A. & Eiselt, K. (eds.) Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, 313–318. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Franks, B. (1995). Sense Generation; A 'Quasi-Classical' Approach to Concepts and Concepts Combination. Cognitive Science 19: 441–505.

    Google Scholar 

  • Gagné</del>, C. L. (2000). Relation-Based Combinations Versus Property-Based Combinations: A Test of the CARIN Theory and Dual-Process Theory of Conceptual Combination. Journal of Memory and Language 42: 365–389.

    Google Scholar 

  • Gagnéé</del>, 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: 71–87.

    Google Scholar 

  • Gazdar, G., Klein, E., Pullum, G. & Sag, I. (1985). Generalized Phrase Structure Grammar. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Giora, R. (1999). On the Priority of Salient Meanings: Studies of Literal and Figurative Language. Journal of Pragmatics 31: 919–929.

    Google Scholar 

  • Grice, H. P. (1975). Logic and Conversation. In Cole, P. & Morgan, J. L. (eds.) Syntax and Semantics. Vol. 3: Speech Acts, 41–58. New York: Academic Press.

    Google Scholar 

  • Keane, M. & Costello, F. (2001). Setting Limits on Analogy: Why Conceptual Combination is not Structural Alignment. In Gentner, D., Holyoak, K. J. & Kokinov, B. (eds.) The Analogical Mind: Perspectives from Cognitive Science, 287–312. Cambridge, MA: MIT Press.

    Google Scholar 

  • Keane, M. T., Ledgeway, T. & Duff, S. R. S. (1994). Constraints on Analogical Mapping: A Comparison of Three Models. Cognitive Science 18: 387–438.

    Google Scholar 

  • Kruschke, J. K. (1992). ALCOVE: An Exemplar-based Connectionist Model of Category Learning. Psychological Review 99: 1922–1944.

    Google Scholar 

  • Lin, D. (1998). An Information-Theoretic Definition of Similarity. In Shavlik, J. (ed.) Proceedings of the Fifteenth International Conference on Machine Learning, 296-304.San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  • Markman, A. B. & Gentner, D. (1993). Structural Alignment during Similarity Comparisons.Cognitive Psychology 25: 431–467.

    Google Scholar 

  • McRae, K. & Boisvert, S. (1998). Automatic Semantic Similarity Priming. Journal of Experimental Psychology: Learning, Memory, and Cognition 24: 558–572.

    Google Scholar 

  • Miller, G. A. (1994). Nouns in WordNet: A Lexical Inheritance System. International Journal of Lexicography 3: 245–264.

    Google Scholar 

  • Murphy, G. L. (1988). Comprehending Complex Concepts. Cognitive Science 12: 529–562.

    Google Scholar 

  • Resnik, P. (1998). Semantic Similarity in a Taxonomy: An Information-Bases Measure and its Application to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research 11: 95–130.

    Google Scholar 

  • Rosch, E. (1975). Family Resemblances: Studies in the Internal Structure of Categories. Cognitive Psychology 7: 573–605.

    Google Scholar 

  • Shoben, E. J. (1993). Non-Predicating Conceptual Combinations.The Psychology of Learning and Motivation 29:391–409.

    Google Scholar 

  • Shoben, E. J. & Gagné, C. L. (1997). Thematic Relations and the Creation of Combined Concepts. In Ward, T. B., Smith, S. M. & Vaid, J. (eds.) Creative Thought: An Investigation of Conceptual Structures and Processes, 31–50. Washington, DC: American Psychological Association.

    Google Scholar 

  • Sloman, S. A., Love, B. C. & Ahn, W.-K. (1998). Feature Centrality and Conceptual Coherence. </del>Cognitive Science 22: 189–228.

    Google Scholar 

  • Tagalakis, G. & Keane, M. T. (2003). Modelling the Understanding of Noun-Noun Compounds: The Role of Familiarity. In Schmalhofer, F., Young, R. M. & Katz, G. (eds.) Proceedings of the European Cognitive Science Conference 2003, 319–324.Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Thompson-Schill, S. L., Kurtz, K. J. & Gabrieli, J. D. E. (1998). Effects of Semantic and Associative Relatedness on Automatic Priming. Journal of Memory and Language 38: 440–458.

    Google Scholar 

  • Wisniewski, E. J. (1996). Construal and Similarity in Conceptual Combination. Journal of Memory and Language 35: 434–453.

    Google Scholar 

  • Wisniewski, E. J. (1997). When Concepts Combine. Psychonomic Bulletin and Review 4: 167–183.

    Google Scholar 

  • Wisniewski, E. J. & Love, B. C. (1998). Relations versus Properties in Conceptual Combination. Journal of Memory and Language 38: 177–202.

    Google Scholar 

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Authors
  1. Dermot Lynott
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  2. Georgios Tagalakis
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  3. Markt. Keane
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Lynott, D., Tagalakis, G. & Keane, M. Conceptual Combination with PUNC. Artificial Intelligence Review 21, 353–374 (2004). https://doi.org/10.1023/B:AIRE.0000036263.74312.50

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  • DOI: https://doi.org/10.1023/B:AIRE.0000036263.74312.50

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