Paper: Incremental Bayesian Learning of Semantic Categories

ACL ID E14-1027
Title Incremental Bayesian Learning of Semantic Categories
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014

Models of category learning have been ex- tensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper we focus on categories acquired from natural lan- guage stimuli, that is words (e.g., chair is a member of the FURNITURE category). We present a Bayesian model which, un- like previous work, learns both categories and their features in a single process. Our model employs particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference in an incremental setting. Comparison against a state-of-the-art graph-based approach re- veals that our model learns qualitatively better categories and demonstrates cogni- tive plausibility during learning.