creators_name: Farkas, Igor creators_name: Li, Ping type: confpaper datestamp: 2001-11-23 lastmod: 2011-03-11 08:54:50 metadata_visibility: show title: A self-organizing neural network model of the acquisition of word meaning ispublished: pub subjects: comp-sci-lang subjects: comp-sci-neural-nets subjects: ling-sem subjects: psy-ling full_text_status: public keywords: word meaning, acquisition, self-organizing neural net, word co-occurrences abstract: In this paper we present a self-organizing connectionist model of the acquisition of word meaning. Our model consists of two neural networks and builds on the basic concepts of Hebbian learning and self-organization. One network learns to approximate word transition probabilities, which are used for lexical representation, and the other network, a self-organizing map, is trained on these representations, projecting them onto a 2D space. The model relies on lexical co-occurrence information to represent word meanings in the lexicon. The results show that our model is able to acquire semantic representations from both artificial data and real corpus of language use. In addition, the model demonstrates the ability to develop rather accurate word representations even with a sparse training set. date: 2001 date_type: published pagerange: 67-72 refereed: TRUE citation: Farkas, Igor and Li, Ping (2001) A self-organizing neural network model of the acquisition of word meaning. [Conference Paper] document_url: http://cogprints.org/1914/1/iccm2001.ps.gz