Conference Proceedings

A Bayesian model for joint learning of categories and their features

L Frermann, M Lapata

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies | The Association for Computational Linguistics | Published : 2015


© 2015 Association for Computational Linguistics. Categories such as ANIMAL or FURNITURE are acquired at an early age and play an important role in processing, organizing, and conveying world knowledge. Theories of categorization largely agree that categories are characterized by features such as function or appearance and that feature and category acquisition go hand-in-hand, however previous work has considered these problems in isolation. We present the first model that jointly learns categories and their features. The set of features is shared across categories, and strength of association is inferred in a Bayesian framework. We approximate the learning environment with natural language ..

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Funding Acknowledgements

We thank Micha Elsner and Charles Sutton for helpful discussions, William Schuler for his comments, and Carina Silberer for providing the Strudel features. We acknowledge the support of EPSRC through project grant EP/I037415/1.