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Child Acquisition of Multiword Verbs: A Computational Investigation

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Cognitive Aspects of Computational Language Acquisition

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Abstract

Traditional theories of grammar, as well as computational modelling of language acquisition, have focused either on aspects of word learning, or grammar learning. Work on intermediate linguistic constructions (the area between words and combinatory grammar rules) has been very limited. Although recent usage-based theories of language learning emphasize the role of multiword constructions, much remains to be explored concerning the precise computational mechanisms that underlie how children learn to identify and interpret different types of multiword lexemes. The goal of the current study is to bring in ideas from computational linguistics on the topic of identifying multiword lexemes, and to explore whether these ideas can be extended in a natural way to the domain of child language acquisition. We take a first step toward computational modelling of the acquisition of a widely-documented class of multiword verbs, such as take the train and give a kiss, that children must master early in language learning. Specifically, we show that simple statistics based on the linguistic properties of these multiword verbs are informative for identifying them in a corpus of child-directed utterances. We present preliminary experiments demonstrating that such statistics can be used within a word learning model to learn associations between meanings and sequences of words.

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Notes

  1. 1.

    A compositional approach to take the train would depend on knowledge of a very specialized meaning of take restricted to occur with a narrow range of objects, which is essentially an alternative lexicalization of the necessary knowledge. See Fazly et al. [31] for a computational approach to the restricted productivity of such expressions.

  2. 2.

    For example, adult competence with the language includes the knowledge that this refers to a single occurrence of a bounded ‘shouting’ action [12, 76].

  3. 3.

    The choice of verb can vary among dialects of the language; for example, British speakers typically say take a decision instead of make a decision and have a nap instead of take a nap.

  4. 4.

    Although it remains to be tested whether children actually do this, a construction grammar approach to language acquisition, as in Goldberg [41], supports this type of calculation, since the learner would keep track of which nouns can occur in which constructions.

  5. 5.

    For example, the verb–noun pair give-hand may occur as an abs usage (give me a hand cleaning up) or as a lit usage (give me Mr. PotatoHead’s hand or give me your pretty hands). In most cases of such potential ambiguity, the annotator had a clear intuition of which would be the predominant usage, since the alternative would be odd to find in CDS. In some cases, such as give-hand, the actual corpus usages were examined to determine the most frequent class.

  6. 6.

    The original model of Fazly et al. treats utterances as unordered bags of words, ignoring syntactic information. Syntax is arguably a valuable source of knowledge in word learning in children (e.g., [39, 56]). In a preliminary study, Alishahi and Fazly [2] also show that the word learning model can potentially benefit from knowledge of syntactic categories. Such information might be necessary for the acquisition of multiword lexemes, and should be further investigated in the future.

  7. 7.

    Following Fazly et al. [33] we assume that words such as a and is also have corresponding meaning symbols in the scene. Such words are often considered by linguists to mainly have a grammatical function. However, it is reasonable to assume that language learners perceive some aspects of their meaning (e.g., definite/indefinite for a determiner such as a, and state/action for the verb be) from the scene.

  8. 8.

    We did not incorporate the Fixed measure into this probability, because this measure needs to consider the usage pattern across several occurrences, and many of the experimental items in this corpus have frequency of only 1 or 2.

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Authors and Affiliations

  1. Department of Computer Science, University of Toronto, Toronto, Canada

    Aida Nematzadeh & Suzanne Stevenson

  2. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), 19395-5746, Tehran, Iran

    Afsaneh Fazly

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  1. Aida Nematzadeh
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  3. Suzanne Stevenson
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Correspondence to Aida Nematzadeh .

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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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Nematzadeh, A., Fazly, A., Stevenson, S. (2013). Child Acquisition of Multiword Verbs: A Computational Investigation. 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_9

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