Symbolic Learning for Natural Language Processing: Integrating Information Extraction and Querying

自然语言处理的符号学习：集成信息提取和查询

• 批准号: 9704943
• 负责人: Raymond Mooney
• 金额: $ 33.96万
• 依托单位: University of Texas at Austin
• 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: 1997-08-01 至 2001-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9704943&HistoricalAwards=false
• 关键词: Symbolic; Learning; Natural; Language; Processing

Recent research indicates that approaches employing automated learning and training are the most promising methods for developing robust, efficient systems for processing natural language. The goal of this research project is to develop symbolic learning methods to aid the construction of natural language systems. Learning algorithms are being developed for constructing systems requiring extraction of information from natural-language documents and subsequent natural-language querying of the resulting database. A method for learning to parse natural language database questions into a formal query language has been developed and is being extended to automate the acquisition of the requisite lexicon of word meanings. Techniques are also being developed for automatically learning rules that locate relevant pieces of data in natural-language documents. Together, these methods can be used to automate the construction of systems requiring natural-language information extraction and querying. Methods for intelligently selecting informative training examples are also being developed in order to reduce the amount of annotated training data required. The resulting methods are being applied to automate the construction of systems that build a database from messages posted to an electronic newsgroup and then respond to natural language queries, resulting in more natural and effective access to electronic information.

最近的研究表明，采用自动化学习和培训的方法是 最有前途的方法，开发强大的，有效的系统，处理自然 语言 本研究项目的目标是开发符号学习方法，以帮助 自然语言系统的构建。 学习算法正在开发， 构建需要从自然语言文档中提取信息的系统， 对结果数据库的后续自然语言查询。 一种学习语法分析的方法 自然语言数据库问题转换成正式的查询语言已经开发出来， 被扩展为自动获取所需的词义词典。 技术也正在开发自动学习规则，定位相关的片段 自然语言文档中的数据。 总之，这些方法可用于自动化 构建需要自然语言信息提取和查询的系统。 智能地选择信息丰富的训练示例的方法也在开发中。 以减少所需的带注释的训练数据的量。 产生的方法是 被应用于从消息构建数据库的系统的自动化构建 发布到电子新闻组，然后响应自然语言查询， 更自然、更有效地获取电子信息。