CAREER: Bayesian Models for Lexicalized Grammars

职业：词汇化语法的贝叶斯模型

• 批准号: 1053856
• 负责人: Julia Hockenmaier
• 金额: $ 50万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1053856&HistoricalAwards=false
• 关键词: CAREER; Bayesian; Models; Lexicalized; Grammars

Natural language processing (NLP) is a key technology for the digital age. At the core of most NLP systems is a parser, a program which identifies the grammatical structure of sentences. Parsing is an essential prerequisite for language understanding. But despite significant progress in recent decades, accurate wide-coverage parsing for any genre or language remains an unsolved problem. The broader impact of this CAREER project will be to advance the state of art in NLP technology through the development of more accurate statistical parsing models. Since language is highly ambiguous, parsers require a statistical model which assigns the highest probability to the correct structure of each sentence. The accuracy of current parsers is limited by the amount of available training data on which their models can be trained, and by the amount of information the models take into account. This CAREER project aims to advance parsing by developing novel methods of indirect supervision to overcome the lack of labeled training data, as well as new kinds of models which incorporate information about the prior linguistic context in which sentences appear. It employs Bayesian techniques, which give robust estimates and allow rich parametrization, and applies them to lexicalized grammars, which provide a compact representation of the syntactic properties of a language.This CAREER project will also train graduate students in natural language processing and develop materials that can be used to teach middle and high school students about NLP and to inspire them to pursue an education in computer science.

自然语言处理（NLP）是数字时代的关键技术。大多数NLP系统的核心是解析器，这是一个识别句子语法结构的程序。句法分析是语言理解的必要前提。但是，尽管近几十年来取得了重大进展，但对任何体裁或语言进行准确的大范围解析仍然是一个未解决的问题。这个CAREER项目的更广泛的影响将是通过开发更准确的统计解析模型来推进NLP技术的发展。 由于语言是高度模糊的，分析器需要一个统计模型，它为每个句子的正确结构分配最高的概率。 当前解析器的准确性受到其模型可以训练的可用训练数据量以及模型考虑的信息量的限制。这个CAREER项目旨在通过开发新的间接监督方法来克服标记训练数据的缺乏，以及新的模型，这些模型包含有关句子出现的先验语言背景的信息，从而促进分析。它采用贝叶斯技术，提供稳健的估计和丰富的参数化，并将其应用于词汇化语法，提供语言句法属性的紧凑表示。该CAREER项目还将培训自然语言处理的研究生，并开发可用于教授初中和高中学生NLP的材料，并激励他们接受计算机科学教育。