Title
Lexical categories at the edge of the word.
Abstract
Language acquisition may be one of the most difficult tasks that children face during development. They have to segment words from fluent speech, figure out the meanings of these words, and discover the syntactic constraints for joining them together into meaningful sentences. Over the past couple of decades, computational modeling has emerged as a new paradigm for gaining insights into the mechanisms by which children may accomplish these feats. Unfortunately, many of these models assume a computational complexity and linguistic knowledge likely to be beyond the abilities of developing young children. This article shows that, using simple statistical procedures, significant correlations exist between the beginnings and endings of a word and its lexical category in English, Dutch, French, and Japanese. Therefore, phonetic information can contribute to individuating higher level structural properties of these languages. This article also presents a simple 2-layer connectionist model that, once trained with an initial small sample of words labeled for lexical category, can infer the lexical category of a large proportion of novel Words using only word-edge phonological information, namely the first and last phoneme of a word. The results suggest that simple procedures combined with phonetic information perceptually available to children provide solid scaffolding for emerging lexical categories in language development.
Year
DOI
Venue
2008
10.1080/03640210701703691
COGNITIVE SCIENCE
Keywords
Field
DocType
phonological bootstrapping,lexical categories,computational models,language acquisition,cross-linguistic corpus analyses,statistical learning,neural networks
Lexical choice,Morpheme,Computer science,Lexical item,Part of speech,Language acquisition,Lexicon,Artificial intelligence,Natural language processing,Lexical chain,Lexical density,Linguistics
Journal
Volume
Issue
ISSN
32
1.0
0364-0213
Citations 
PageRank 
References 
3
0.41
6
Authors
2
Name
Order
Citations
PageRank
Luca Onnis152.83
Morten H. Christiansen226944.17