CAREER: Building Conceptual Natural Language Processing Systems for Practical Applications

职业：为实际应用构建概念自然语言处理系统

• 批准号: 9704240
• 负责人: Ellen Riloff
• 金额: $ 47.72万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-05-15 至 2003-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9704240&HistoricalAwards=false
• 关键词: CAREER; Building; Conceptual; Natural; Language

This research aims to develop a conceptual natural language processing (NLP) system with adaptable components that can be easily tailored for different domains and applications. The architecture of the system consists of fine-grained layers to support various depths of text processing. The shallow layers support syntactic processing, which may be sufficient for some information retrieval tasks, while the deeper layers support semantic and conceptual processing for in-depth language understanding. The system includes components for part-of-speech tagging, prepositional phrase attachment, semantic feature identification, and concept extraction. Each component can be tailored for new domains with minimal manual effort. The layered architecture also allows students to develop individual components and plug them in to the larger system for experimentation. The education goals are to use the system as the basis for a hands-on science workshop for young girls, for summer lectures to high school students, for class projects in natural language processing and machine learning, and for graduate and undergraduate research projects. The purpose of the research is to develop techniques for building conceptual natural language understanding systems automatically or semi-automatically for new domains. Generating conceptual sentence analyzers quickly and efficiently is an important step toward many practical applications, including conceptual information retrieval, text categorization, and information extraction.

本研究旨在开发一个概念性的自然语言处理（NLP）系统，该系统具有可适应的组件，可以很容易地为不同的领域和应用程序量身定制。 该系统的体系结构由细粒度层组成，以支持各种深度的文本处理。 浅层支持语法处理，这对于某些信息检索任务可能是足够的，而深层支持语义 和 概念处理 语言理解 该系统包括用于词性标记的组件， 介词短语附加，语义的 特征识别和概念提取。每个组件都可以通过最少的手动工作量针对新领域进行定制。 的分层架构 也 允许学生 发展 单个组分 和 把它们插到更大的 系统 做实验教育目标是使用该系统作为年轻女孩动手科学研讨会的基础，为高中生举办夏季讲座，自然语言处理和机器学习课程项目，以及研究生和本科生研究项目。该研究的目的是开发用于为新领域自动或半自动构建概念自然语言理解系统的技术。 快速有效地生成概念句子分析器 是 的重要一步 许多 实际应用，包括概念信息检索，文本分类和信息提取。