Knowledge Acquisition for Natural Language Understanding

构建能够读取、总结和从文本中提取信息的健壮系统的一个主要障碍是需要大量的语言知识来处理几乎遍及文本分析所有方面的无数语法、语义和语用歧义。本研究的目的是解决自然语言处理（NLP）系统的知识工程瓶颈。这项工作扩展了一个通用的知识获取框架，该框架允许NLP系统使用标准的归纳机器学习技术，结合带注释的语料库和健壮的句子分析，直接从文本中引导自己的知识库。特别是，该框架正在扩展以处理词法和结构歧义解决方面的其他问题，并且正在使用Penn Treebank数据在更大的NLP任务上下文中进行评估。本文的研究具有重要的理论意义和现实意义。首先，研究将开始确定在哪些条件下机器学习技术可以为NLP系统提供一种具有成本效益的知识获取方法，特别是与现有的统计技术相比。其次，这项工作将把当前的系统扩展成一个集成的工具，使用机器学习技术来指导NLP系统的开发。

A major obstacle to building robust systems that can read, summarize, and extract information from text is the need for large amounts of linguistic knowledge to handle the myriad syntactic, semantic, and pragmatic ambiguities that pervade virtually all aspects of text analysis. The objective of this research is to address this knowledge engineering bottleneck for natural language processing (NLP) systems. The work extends a general knowledge acquisition framework that allows an NLP system to bootstrap its own knowledge bases directly from text using standard inductive machine learning techniques in conjunction with an annotated corpus and robust sentence analysis. In particular, the framework is being extended to handle additional problems in lexical and structural ambiguity resolution and is being evaluated using Penn Treebank data within the context of a larger NLP task. The work is of both theoretical and practical significance. First, the research will begin to determine the conditions under which machine learning techniques can be expected to offer a cost-effective approach to knowledge acquisition for NLP systems, especially in comparison to existing statistical techniques. Second, the work will expand the current system into an integrated tool that uses machine learning techniques to guide NLP system development.