Syntactic categorization in early language acquisition: Formalizing the role of distributional analysis
Syntactic categorization in early language acquisition: Formalizing the role of distributional analysis
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DOI：
10.1016/s0010-0277(96)00793-7
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发表时间：
1997-05-01
期刊：
影响因子：
3.4
通讯作者：
Brent, MR
中科院分区：
文献类型：
--
作者：
Cartwright, TA;Brent, MR
我们提出了一种明确的，增量策略，通过这种策略，儿童可以将具有相似句法特权的单词分组为离散的，未标记的类别。这种可以发现词汇歧义的策略部分是基于句子最小对概念的推广。因此，它在开始分类时对语法知识的可用性做了最小的假设。虽然所提出的策略是分布式的，但它可以利用来自其他领域的分类线索，包括语义和音系。计算机模拟表明，这种策略在对人工语言样本和自然发生的儿童导向语音文本中的单词进行分类方面都是有效的。此外，仿真表明，当提供具体名词的语义信息时，所提出的策略表现得更好。讨论了对分类理论的启示。
We propose an explicit, incremental strategy by which children could group words with similar syntactic privileges into discrete, unlabeled categories. This strategy, which can discover lexical ambiguity, is based in part on a generalization of the idea of sentential minimal pairs. As a result, it makes minimal assumptions about the availability of syntactic knowledge at the onset of categorization. Although the proposed strategy is distributional, it can make use of categorization cues from other domains, including semantics and phonology. Computer simulations show that this strategy is effective at categorizing words in both artificial-language samples and transcripts of naturally-occurring, child-directed speech. Further, the simulations show that the proposed strategy performs even better when supplied with semantic information about concrete nouns. Implications for theories of categorization are discussed.