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LODES of grammar: Syntactic error analysis for intelligent computer-assisted language instruction.

机译:语法的高度:智能计算机辅助语言教学的句法错误分析。

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摘要

The objective of this thesis is to investigate a principled theory of error analysis for Intelligent Computer-Assisted Language Instruction (ICALI). The research question addresses: how can a natural language processing program, namely LODES, can detect and classify errors in syntax according to a model of Universal Grammar based on the Principles and Parameters of Government and Binding theory? This is a feasibility study demonstrating how an error-detection ICALI program can make judgements of grammaticality and report violations of well-formed sentences in a manner similar to a human tutor. In the past error detection for conventional Computer Assisted Language Instruction (CALI) has been limited by reliance on many rules that describe the surface structure idiosyncracies of student input. CALI programs have also been restricted by the lack of a linguistic model. The present research attempts to overcome these shortcomings by relying on the modular linguistic theory of Noam Chomsky and techniques from Artificial Intelligence research that perform natural language processing. Drawing on both theoretical linguistics and artificial intelligence we show how to detect errors and infer an explanation for the cause of an error based principles and parameters rather than rules. Our data comes from native Spanish-speaking undergraduates at the University of Puerto Rico enrolled in low level English as a foreign language classes. Our analysis of student errors is a result of the modularity of the natural language processor which allows us to pinpoint a particular error type as a violation of a particular linguistic principle because the module in which the error occurs shows a particular kind of violation that we can classify. We find that a third of the sample sentences could be described and explained as violations of one or more principles elaborated in Universal Grammar theory. The remaining sentences demonstrate that the errors made by beginning second language learners are too numerous to be handled by a fixed order application of principles. It is recommended that additional computational techniques be combined with a modular linguistic theory to increase the number and type of sentences that can be handled.
机译:本文的目的是研究智能计算机辅助语言教学(ICALI)的错误分析原理。该研究问题针对:自然语言处理程序(即LODES)如何根据政府和约束理论和参数的通用语法模型对语法错误进行检测和分类?这是一项可行性研究,演示了错误检测ICALI程序如何以类似于人工指导的方式做出语法判断并报告格式错误的句子。过去,传统的计算机辅助语言指令(CALI)的错误检测受到许多描述学生输入的表面结构特质的规则的限制。缺少语言模型也限制了CALI程序。本研究试图通过依靠Noam Chomsky的模块化语言理论和执行自然​​语言处理的人工智能研究技术来克服这些缺点。借助理论语言学和人工智能,我们展示了如何检测错误并根据错误的原理和参数(而非规则)推断出错误原因。我们的数据来自波多黎各大学讲西班牙语的本科生,他们以低级英语作为外语课程就读。我们对学生错误的分析是自然语言处理器模块化的结果,该模块使我们能够将特定错误类型确定为对特定语言原理的违反,因为发生错误的模块显示出我们可以分类。我们发现三分之一的例句可能被描述和解释为违反了通用语法理论中阐述的一个或多个原理。其余的句子表明,初学者学习第二语言所犯的错误太多了,无法通过固定顺序的原则来处理。建议将其他计算技术与模块化语言理论结合使用,以增加可以处理的句子的数量和类型。

著录项

  • 作者

    Arzan, Angel M.;

  • 作者单位

    New York University.;

  • 授予单位 New York University.;
  • 学科 Education Language and Literature.;Computer Science.;Artificial Intelligence.;Education Technology of.;Language Linguistics.
  • 学位 Ph.D.
  • 年度 1992
  • 页码 333 p.
  • 总页数 333
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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