Learning the meaning of words from ambiguous and noisy context is a challenging task for language learners. It has been suggested that children draw on syntactic cues such as lexical categories of words to constrain potential referents of words in a complex scene. Although the acquisition of lexical categories should be interleaved with learning word meanings, it has not previously been modeled in that fashion. In this paper, we investigate the interplay of word learning and category induction by integrating an LDA-based word class learning module with a probabilistic word learning model. Our results show that the incrementally induced word classes significantly improve word learning, and their contribution is comparable to that of manually assigned part of speech categories.
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