A Probabilistic Computational Model of Cross-Situational Word Learning
A Probabilistic Computational Model of Cross-Situational Word Learning
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DOI：
10.1111/j.1551-6709.2010.01104.x
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发表时间：
2010-08-01
影响因子：
2.5
通讯作者：
Stevenson, Suzanne
中科院分区：
文献类型：
--
作者：
Fazly, Afsaneh;Alishahi, Afra;Stevenson, Suzanne
单词是交流的本质：它们是任何语言的基石。因此，学习单词的意思是语言习得最重要的方面之一：孩子们必须首先学习单词，然后才能将它们组合成复杂的话语。为了解释幼儿在习得母语词汇方面令人印象深刻的效率，以及在词汇习得过程中观察到的发展模式，人们发展了许多理论。不同理论之间分歧的一个主要来源是，儿童是否具备特殊的词汇学习机制和偏见，还是他们的一般认知能力足以完成这项任务。我们提出了一个新的早期单词学习的计算模型，以阐明在这个过程中可能起作用的机制。该模型使用增量和概率学习机制，仅利用一般认知能力，将词义作为单词和语义元素之间的概率关联来学习。本研究的结果表明，在不使用任何特殊偏见或限制的情况下，可以从自然发生的儿童定向话语（与意义表征配对）中学习到很多关于词义的知识，并且在潜在的学习机制中没有任何明显的发展变化。此外，我们的模型为偶尔矛盾的儿童实验数据提供了解释，并为年轻的单词学习者在新情况下的行为提供了预测。
Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.