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

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Cognitive Aspects of Computational Language Acquisition

Abstract

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.

This research was conducted while the first author was a graduate student at University College Dublin.

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Notes

  1. 1.

    There are exceptions where the type of relation differs depending on word order; these tend to be lexicalized compounds, or compounds containing a polysemous word where the sense in the modifier position can differ from the sense in the head position (e.g. guitar solo and solo guitar).

  2. 2.

    Other exemplar modelling frameworks, such as the Diagnostic Evidence Model [28] and TiMBL [33] could also have be investigated. However, comparing different modelling frameworks on this task lies beyond the scope of this chapter.

  3. 3.

    If a participant rates two or more relations with the same maximal likelihood, we assume the participant would select at random between them. Whether or not the data are actually transformed in this way makes only small differences to the fit of the model reported subsequently.

  4. 4.

    For example, the relation vector for exemplars occurring with Relation 1 (i.e. the beetle and plant exemplars in items 1, 2, 3, 5, 6 and 7) would be \(\left [1, 0, 0\right ]\).

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Acknowledgements

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

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Authors and Affiliations

  1. Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, UK

    Barry J. Devereux

  2. School of Computer Science and Informatics, University College Dublin, Dublin, Ireland

    Fintan J. Costello

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  1. Barry J. Devereux
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  2. Fintan J. Costello
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Correspondence to Barry J. Devereux .

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Editors and Affiliations

  1. Institute of Informatics, Federal University of Rio Grande do Sul, Av. Bento Gonçalves, Porto Alegre, 9500, Brazil

    Aline Villavicencio

  2. Universite Sorbonne Nouvelle, LATTICE-CNRS, Ecole Normale Superieure and, rue d'Ulm 45, Paris, 75005, France

    Thierry Poibeau

  3. Computer Laboratory, William Gates Building, University of Cambridge, Thomson Avenue 15 JJ, Cambridge, CB3 0FD, United Kingdom

    Anna Korhonen

  4. and Communication (TiCC), Tilburg University, Tilburg center for Cognition, Warandelaan 2, Tilburg, 5037, Netherlands

    Afra Alishahi

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Devereux, B.J., Costello, F.J. (2012). Learning to Interpret Novel Noun-Noun Compounds: Evidence from Category Learning Experiments. In: Villavicencio, A., Poibeau, T., Korhonen, A., Alishahi, A. (eds) Cognitive Aspects of Computational Language Acquisition. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31863-4_8

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