Constraint Use In Language Comprehension

本研究旨在探讨正常成人语言理解中句法歧义的消解。歧义消解的研究可以阐明语言理解系统的结构，并可以评价心理语言学和语言学关于语言心理表征本质的理论。 该研究的理论框架是一个词汇的、基于约束的框架，其中句法歧义被看作是词汇歧义的一种类型，因此句法歧义的消解与词汇语义歧义的消解具有相同的性质。这种方法已被应用于分析英语中的几个“经典”的句法歧义，以前被认为是一个根本不同的处理架构的证据。 实验采用自定进度的阅读方法和眼注视监测来研究歧义消解和约束使用。大量的规范性数据也收集有关的约束的强度，并使用回归方法来确定在何种程度上不同的约束影响的解释者在不同的时间点。 这一研究的意义在于，它提出了一个统一的语言理解理论，在语言理解中，各个层面（语音、词汇、句法、语篇等）的表征都是语言理解的重要组成部分。由相同的基本属性，包括频率敏感性和通过约束满足的表示激活。对语言表征的统一解释不仅影响了人类语言使用的理论，也影响了人工智能的工作，迄今为止，人工智能通常反映了人类语言理解的传统理论。 因此，我们对人类歧义消解统一方法的理解可能对计算机自然语言理解研究产生重大影响，特别是这些系统中非常困难的歧义消解问题。

This research is designed to investigate syntactic ambiguity resolution in normal, adult language comprehension. The study of ambiguity resolution can illuminate the architecture of the language comprehension system and can evaluate the psycholinguistic and linguistic theories concerning the nature of linguistic mental representations. The theoretical framework of the investigation is a lexical, "constraint-based" framework in which syntactic ambiguity is seen as a type of lexical ambiguity, such that syntactic ambiguity resolution has the same properties as the resolution of lexical semantic ambiguities. This approach has been applied profitably to the analysis of several "classic" syntactic ambiguities in English that had previously been taken as evidence for a radically different processing architecture. The experiments use self-paced reading methods and eye fixation monitoring to study ambiguity resolution and constraint use. A large amount of normative data is also collected concerning the strength of the constraints, and regression methods are used to determine the extent to which difference constraints affect comprehenders' interpretations at different points in time. The research is significant in that it advances a unified account of language comprehension in which representation at all levels (phonological, lexical, syntactic, discourse, etc.) are governed by the same basic properties, including frequency sensitivity and activation of representations via constraint satisfaction. A unified account of language representation affects not only theories of human language use, but also work in artificial intelligence, which to date has generally reflected the more traditional theories of human language comprehension. Our understanding in the unified approach of human ambiguity resolution could therefore have substantial impact on computer natural language comprehension research, particularly the very difficult problem of ambiguity resolut ion in these systems.