Paper: A Probabilistic Model of Syntactic and Semantic Acquisition from Child-Directed Utterances and their Meanings

ACL ID E12-1024
Title A Probabilistic Model of Syntactic and Semantic Acquisition from Child-Directed Utterances and their Meanings
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

This paper presents an incremental prob- abilistic learner that models the acquis- tion of syntax and semantics from a cor- pus of child-directed utterances paired with possible representations of their meanings. These meaning representations approxi- mate the contextual input available to the child; they do not specify the meanings of individual words or syntactic derivations. The learner then has to infer the meanings and syntactic properties of the words in the input along with a parsing model. We use the CCG grammatical framework and train a non-parametric Bayesian model of parse structure with online variational Bayesian expectation maximization. When tested on utterances from the CHILDES corpus, our learner outperforms a state-of-the-art se- mantic parser. In addition, it models such...