[论文解读] A Computational Model of Syntactic Processing: Ambiguity Resolution from Interpretation

Michael Niv|ArXiv.org|Jun 20, 1994

Natural Language Processing Techniques参考文献 96被引用 9

一句话总结

本文提出了一种句法处理的计算模型，其中歧义消解由语义解释驱动，而非句法偏好。该模型假设一个简单、盲目的句法解析器，生成至当前点的所有可能分析，而基于语义的解释模块则过滤掉较不可能的分析，成功预测了花园路径句的处理难度，并挑战了结构偏好理论。

ABSTRACT

Syntactic ambiguity abounds in natural language, yet humans have no difficulty coping with it. In fact, the process of ambiguity resolution is almost always unconscious. But it is not infallible, however, as example 1 demonstrates. 1. The horse raced past the barn fell. This sentence is perfectly grammatical, as is evident when it appears in the following context: 2. Two horses were being shown off to a prospective buyer. One was raced past a meadow. and the other was raced past a barn. ... Grammatical yet unprocessable sentences such as 1 are called `garden-path sentences.' Their existence provides an opportunity to investigate the human sentence processing mechanism by studying how and when it fails. The aim of this thesis is to construct a computational model of language understanding which can predict processing difficulty. The data to be modeled are known examples of garden path and non-garden path sentences, and other results from psycholinguistics. It is widely believed that there are two distinct loci of computation in sentence processing: syntactic parsing and semantic interpretation. One longstanding controversy is which of these two modules bears responsibility for the immediate resolution of ambiguity. My claim is that it is the latter, and that the syntactic processing module is a very simple device which blindly and faithfully constructs all possible analyses for the sentence up to the current point of processing. The interpretive module serves as a filter, occasionally discarding certain of these analyses which it deems less appropriate for the ongoing discourse than their competitors. This document is divided into three parts. The first is introductory, and reviews a selection of proposals from the sentence processing literature. The second part explores a body of data which has been adduced in support of a theory of structural preferences --- one that is inconsistent with the present claim. I show how the current proposal can be specified to account for the available data, and moreover to predict where structural preference theories will go wrong. The third part is a theoretical investigation of how well the proposed architecture can be realized using current conceptions of linguistic competence. In it, I present a parsing algorithm and a meaning-based ambiguity resolution method.

研究动机与目标

• 开发一种句法处理的计算模型，以预测人类在歧义句中的处理难度。
• 挑战主流观点，即句法解析依赖于结构偏好，提出歧义消解由语义解释驱动。
• 证明即使不使用偏好启发式方法，句法解析器也能通过解释性过滤有效引导。
• 基于当前语言学和计算原理，提供一个理论上有根基的语言理解架构。
• 通过基于语义的消歧机制，解释心理语言学中关于花园路径句的数据。

提出的方法

• 实现一个句法解析器，生成至当前处理点的所有可能句法分析，且不应用偏好启发式方法。
• 引入一个独立的解释模块，基于语义合理性与语篇语境评估并过滤句法分析。
• 采用基于语义的歧义消解方法，舍弃较不合适的句法结构，保留更符合当前语篇的结构。
• 设计一种自下而上、逐步进行的解析算法，在足够语境可用前延迟解释。
• 将语言能力模型与处理机制整合，确保与当前语言学理论在理论上的一致性。
• 通过已知的心理语言学数据（特别是花园路径句）验证模型，以检验处理难度的预测。

实验结果

研究问题

• RQ1 句法处理中的歧义消解能否在不依赖句法结构偏好下得到解释？
• RQ2 如何通过语义解释有效引导生成所有可能分析的句法解析器来解决歧义？
• RQ3 有哪些证据支持‘解释而非解析启发式方法’驱动即时歧义消解的观点？
• RQ4 所提出的模型在多大程度上比结构偏好理论更准确地预测花园路径句的处理难度？
• RQ5 如何形式化一个基于语义的过滤机制，使其能在实时句法处理过程中逐步运行？

主要发现

• 该模型通过依赖语义合理性而非句法偏好启发式方法，成功预测了花园路径句的处理难度。
• 结构偏好理论无法解释某些心理语言学数据，而该模型通过引入解释性过滤机制成功解释了这些现象。
• 句法解析器对所有可能分析的盲生成（至当前点）使得无需假设偏好机制即可准确建模人类处理过程。
• 解释模块能有效过滤掉不合理的句法结构，证明基于语义的消歧机制可驱动歧义消解。
• 该模型能够解释已知的花园路径现象，例如在‘The horse raced past the barn fell’中，歧义仅在足够语篇语境出现后才得以解决。
• 该架构在当前语言能力概念下理论上是可实现的，支持其作为人类句法处理计算模型的可行性。