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使用“频繁框架”对单词进行分类:跨语言分析揭示的分布习得策略

Categorizing words using 'frequent frames': what cross-linguistic analyses reveal about distributional acquisition strategies.

作者信息

Chemla Emmanuel, Mintz Toben H, Bernal Savita, Christophe Anne

机构信息

Laboratoire de Sciences Cognitives et Psycholinguistique, DEC-ENS/EHESS, CNRS, Paris, France.

出版信息

Dev Sci. 2009 Apr;12(3):396-406. doi: 10.1111/j.1467-7687.2009.00825.x.

Abstract

Mintz (2003) described a distributional environment called a frame, defined as the co-occurrence of two context words with one intervening target word. Analyses of English child-directed speech showed that words that fell within any frequently occurring frame consistently belonged to the same grammatical category (e.g. noun, verb, adjective, etc.). In this paper, we first generalize this result to French, a language in which the function word system allows patterns that are potentially detrimental to a frame-based analysis procedure. Second, we show that the discontinuity of the chosen environments (i.e. the fact that target words are framed by the context words) is crucial for the mechanism to be efficient. This property might be relevant for any computational approach to grammatical categorization. Finally, we investigate a recursive application of the procedure and observe that the categorization is paradoxically worse when context elements are categories rather than actual lexical items. Item-specificity is thus also a core computational principle for this type of algorithm. Our analysis, along with results from behavioural studies (Gómez, 2002; Gómez and Maye, 2005; Mintz, 2006), provides strong support for frames as a basis for the acquisition of grammatical categories by infants. Discontinuity and item-specificity appear to be crucial features.

摘要

明茨(2003年)描述了一种称为框架的分布环境,定义为两个上下文单词与一个中间目标单词的共现。对英语儿童导向语的分析表明,处于任何频繁出现框架内的单词始终属于同一语法类别(如名词、动词、形容词等)。在本文中,我们首先将这一结果推广到法语,在这种语言中,功能词系统允许一些可能对基于框架的分析程序不利的模式。其次,我们表明所选环境的不连续性(即目标单词由上下文单词框定这一事实)对于该机制的高效运行至关重要。这一特性可能与任何语法分类的计算方法都相关。最后,我们研究了该程序的递归应用,并观察到当上下文元素是类别而非实际词汇项时,分类反而更差。因此,特定项也是这类算法的核心计算原则。我们的分析以及行为研究的结果(戈麦斯,2002年;戈麦斯和梅伊,2005年;明茨,2006年)为框架作为婴儿获取语法类别的基础提供了有力支持。不连续性和特定项似乎是关键特征。

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