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

最近的研究表明，采用自动学习和训练的方法是开发健壮、高效的自然语言处理系统最有前途的方法。本研究计划的目标是发展符号学习方法，以协助自然语言系统的建构。正在开发学习算法，用于构建需要从自然语言文档中提取信息并随后对结果数据库进行自然语言查询的系统。已经开发了一种学习将自然语言数据库问题解析为正式查询语言的方法，并正在扩展到自动获取必要的词义词典。在自然语言文档中定位相关数据块的自动学习规则的技术也在开发中。总之，这些方法可以用于自动构建需要自然语言信息提取和查询的系统。为了减少所需要的带注释的训练数据的数量，智能地选择信息丰富的训练示例的方法也正在开发中。由此产生的方法被应用于自动化系统的构建，这些系统根据发布到电子新闻组的消息建立数据库，然后对自然语言查询作出响应，从而更自然、更有效地访问电子信息。

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.