Research on Language and Computation

, Volume 8, Issue 2–3, pp 169–207 | Cite as

Computational Models of Learning the Raising-Control Distinction

  • William Garrett Mitchener
  • Misha Becker


We consider the task of learning three verb classes: raising (e.g., seem), control (e.g., try) and ambiguous verbs that can be used either way (e.g., begin). These verbs occur in sentences with similar surface forms, but have distinct syntactic and semantic properties. They present a conundrum because it would seem that their meaning must be known to infer their syntax, and that their syntax must be known to infer their meaning. Previous research with human speakers pointed to the usefulness of two cues found in sentences containing these verbs: animacy of the sentence subject and eventivity of the predicate embedded under the main verb. We apply a variety of algorithms to this classification problem to determine whether the primary linguistic data is sufficiently rich in this kind of information to enable children to resolve the conundrum, and whether this information can be extracted in a way that reflects distinctive features of child language acquisition. The input consists of counts of how often various verbs occur with animate subjects and eventive predicates in two corpora of naturalistic speech, one adult-directed and the other child-directed. Proportions of the semantic frames are insufficient. A Bayesian attachment model designed for a related language learning task does not work well at all. A hierarchical Bayesian model (HBM) gives significantly better results. We also develop and test a saturating accumulator that can successfully distinguish the three classes of verbs. Since the HBM and saturating accumulator are successful at the classification task using biologically realistic calculations, we conclude that there is sufficient information given subject animacy and predicate eventivity to bootstrap the process of learning the syntax and semantics of these verbs.


Bayesian inference Child language acquisition Clustering Control Raising Syntax Unsupervised learning 


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of MathematicsCollege of CharlestonCharlestonUSA
  2. 2.Linguistics DepartmentUniversity of North CarolinaChapel HillUSA

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