Class-based n-gram models of natural language
Computational Linguistics
The Journal of Machine Learning Research
Co-evolution of language and of the language acquisition device
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Modeling and predicting personal information dissemination behavior
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Early lexical development in a self-organizing neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Inducing syntactic categories by context distribution clustering
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
An incremental bayesian model for learning syntactic categories
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Online entropy-based model of lexical category acquisition
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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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.