Paper: An Incremental Bayesian Model for Learning Syntactic Categories

ACL ID W08-2112
Title An Incremental Bayesian Model for Learning Syntactic Categories
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2008
Authors

We present an incremental Bayesian model for the unsupervised learning of syntactic cate- gories from raw text. The model draws infor- mation from the distributional cues of words within an utterance, while explicitly bootstrap- ping its development on its own partially- learned knowledge of syntactic categories. Testing our model on actual child-directed data, we demonstrate that it is robust to noise, learns reasonable categories, manages lexical ambiguity, and in general shows learning be- haviours similar to those observed in children.