On learning the past tenses of English verbs
Parallel distributed processing: explorations in the microstructure of cognition, vol. 2
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
Bayesian spiking neurons i: Inference
Neural Computation
Bayesian spiking neurons ii: Learning
Neural Computation
The acquisition and use of argument structure constructions: a Bayesian model
PMHLA '05 Proceedings of the Workshop on Psychocomputational Models of Human Language Acquisition
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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.