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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.\n"
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chapter: ~
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conference: Fourth International Conference on Cognitive Modeling
confloc: 'Fairfax, Virginia'
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creators_id: []
creators_name:
- family: Farkas
given: Igor
honourific: ''
lineage: ''
- family: Li
given: Ping
honourific: ''
lineage: ''
date: 2001
date_type: published
datestamp: 2001-11-23
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dir: disk0/00/00/19/14
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eprintid: 1914
fileinfo: /style/images/fileicons/application_postscript.png;/1914/1/iccm2001.ps.gz
full_text_status: public
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keywords: 'word meaning, acquisition, self-organizing neural net, word co-occurrences'
lastmod: 2011-03-11 08:54:50
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metadata_visibility: show
note: ~
number: ~
pagerange: 67-72
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reportno: ~
rev_number: 8
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status_changed: 2007-09-12 16:41:41
subjects:
- comp-sci-lang
- comp-sci-neural-nets
- ling-sem
- psy-ling
succeeds: ~
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title: A self-organizing neural network model of the acquisition of word meaning
type: confpaper
userid: 2419
volume: ~