Online Entropy-based Model of Lexical Category Acquisition
Children learn a robust representation of lexical categories at a young age. We propose an incremental model of this process which efficiently groups words into lexical categories based on their local context using an information-theoretic criterion. We train our model on a corpus of child-directed speech from CHILDES and show that the model learns a fine-grained set of intuitive word categories. Furthermore, we propose a novel evaluation approach by comparing the efficiency of our induced categories against other category sets (including traditional part of speech tags) in a variety of language tasks. We show the categories induced by our model typically outperform the other category sets.