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Fuzzy methodology for enhancement of context understanding.

机译:模糊方法可增强对上下文的理解。

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摘要

In recent years, research in the domain of Natural Language Understanding (NLU) has benefited from the methodological compilation of context corpora with rich syntax such as the Brown corpora of the Penn Treebank, and machine-readable semantic lexicons such as WordNet. It has also benefited from developments of parsers for syntactic information presentation and semantic meaning retrieval using such corpora. Both types of activities have also advanced the progress in the comprehension of semantic meanings of natural contexts.; The comprehension of semantic meanings is at the heart of the literature of modern linguistics. Inspired largely by the work of the current view of natural language processing, the focus of language processing is isolated linguistic items, research in statistical literature linguistics has focused primarily on the statistical representation of linguistic occurrences. With the help of a wide range of context corpora, this statistical representation ensures a virtually comprehensive coverage of lexical combinational occurrences, which allows a computer program to identify the recurrence of a combination and to further predict the meaning of the recurrence. Due to the limitations of current computer technology, however, representing a lexical combination is restricted to a finite length. Since the statistical representation is confined to describing a finite term-span context, which impedes a computer program from the understanding of contextual meaning, we focus attention on obtaining an approximate but simpler and more satisfactory solutions through soft computing techniques; in particular, fuzzy set theory.; The proposed fuzzy methodology is based on the definition of fuzzy formalism. This involves the comprehensive study of the interplay between the syntactic structure of a verb and the semantic roles that the verb plays. The theory underlying fuzzy formalism is that the syntactic representations associated with a particular verb indicates an explicit range of semantic meanings. Through applying fuzzy formalism to the training corpora, the quality of quantitative representation of linguistic items is improved. (Abstract shortened by UMI.)
机译:近年来,自然语言理解(NLU)领域的研究受益于具有丰富语法的上下文语料库(例如Penn Treebank的Brown语料库)和机器可读语义词典(例如WordNet)的方法学编译。它也得益于用于此类信息集的语法信息表示和语义含义检索解析器的开发。两种类型的活动也促进了对自然环境语义含义的理解。语义的理解是现代语言学文献的核心。在很大程度上受到当前自然语言处理观点的启发,语言处理的重点是孤立的语言项目,统计文献语言学的研究主要集中在语言出现的统计表示上。在广泛的上下文语料库的帮助下,这种统计表示确保了词法组合出现的几乎全面的覆盖,这使计算机程序可以识别组合的重复并进一步预测重复的含义。然而,由于当前计算机技术的局限性,表示词法组合被限制为有限的长度。由于统计表示仅限于描述有限的术语跨度上下文,这妨碍了计算机程序对上下文含义的理解,因此我们将注意力集中在通过软计算技术获得近似但更简单且更令人满意的解决方案上。特别是模糊集理论。所提出的模糊方法论基于模糊形式主义的定义。这涉及对动词的句法结构与该动词所扮演的语义角色之间的相互作用的全面研究。模糊形式主义的基础理论是,与特定动词相关的句法表示法表示语义范围的明确范围。通过将模糊形式主义应用于训练语料库,提高了语言项目定量表示的质量。 (摘要由UMI缩短。)

著录项

  • 作者

    Sun, Yu.;

  • 作者单位

    University of Waterloo (Canada).;

  • 授予单位 University of Waterloo (Canada).;
  • 学科 Engineering System Science.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 179 p.
  • 总页数 179
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 系统科学;
  • 关键词

  • 入库时间 2022-08-17 11:41:33

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