A Bayesian Model for Joint Learning of Categories and their Features

Lea Frermann, Mirella Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 text which allows us to evaluate performance on a large scale. Compared to highly engineered pattern-based approaches, our model is cognitively motivated, knowledge-lean, and learns categories and features which are perceived by humans as more meaningful.
Original languageEnglish
Title of host publicationProceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Place of PublicationDenver, Colorado
PublisherAssociation for Computational Linguistics
Pages1576-1586
Number of pages11
ISBN (Print)978-1-941643-49-5
Publication statusPublished - 1 May 2015

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