How does linguistic context influence word learning?

被引:1
|
作者
Alhama, Raquel G. G. [1 ]
Rowland, Caroline F. F. [2 ,3 ]
Kidd, Evan [2 ,4 ,5 ]
机构
[1] Tilburg Univ, Dept Cognit Sci & Artificial Intelligence, Tilburg, Netherlands
[2] Max Planck Inst Psycholinguist, Language Dev Dept, Nijmegen, Netherlands
[3] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands
[4] Australian Natl Univ, Canberra, ACT, Australia
[5] ARC Ctr Excellence Dynam Language, Canberra, ACT, Australia
关键词
Word learning; Vector Space Models; semantic networks; LANGUAGE; PREDICTION; FREQUENCY; NETWORKS; CHILDREN; MODELS; INPUT; VERBS;
D O I
10.1017/S0305000923000302
中图分类号
B844 [发展心理学(人类心理学)];
学科分类号
040202 ;
摘要
While there are well-known demonstrations that children can use distributional information to acquire multiple components of language, the underpinnings of these achievements are unclear. In the current paper, we investigate the potential pre-requisites for a distributional learning model that can explain how children learn their first words. We review existing literature and then present the results of a series of computational simulations with Vector Space Models, a type of distributional semantic model used in Computational Linguistics, which we evaluate against vocabulary acquisition data from children. We focus on nouns and verbs, and we find that: (i) a model with flexibility to adjust for the frequency of events provides a better fit to the human data, (ii) the influence of context words is very local, especially for nouns, and (iii) words that share more contexts with other words are harder to learn.
引用
收藏
页码:1374 / 1393
页数:20
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