Incremental Bayesian Category Learning from Natural Language

Autor/inn/en	Frermann, Lea; Lapata, Mirella
Titel	Incremental Bayesian Category Learning from Natural Language
Quelle	In: Cognitive Science, 40 (2016) 6, S.1333-1381 (49 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.12304
Schlagwörter	Classification; Bayesian Statistics; Models; Cognitive Science; Monte Carlo Methods; Learning + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Kognitionswissenschaft; Monte-Carlo-Methode; Lernen
Abstract	Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words (e.g., "chair" is a member of the furniture category). We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: (a) the acquisition of features that discriminate among categories, and (b) the grouping of concepts into categories based on those features. Our model learns categories "incrementally" using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (An earlier version of this work was published in Frermann and Lapata [Frermann, L., 2014].) (As Provided).
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Update	2020/1/01