CAREER: Bayesian Models for Lexicalized Grammars

自然语言处理（NLP）是数字时代的一项关键技术。大多数NLP系统的核心是一个解析器，一个识别句子语法结构的程序。解析是理解语言的必要前提。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的准确、广泛的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动NLP技术的发展。由于语言是高度模糊的，解析器需要一个统计模型，该模型将最大概率分配给每个句子的正确结构。当前解析器的准确性受到可用训练数据的数量的限制，模型可以在这些数据上进行训练，而且模型考虑的信息量也有限。这个CAREER项目旨在通过开发新的间接监督方法来推进解析，以克服标记训练数据的缺乏，以及包含有关句子出现的先前语言上下文信息的新型模型。它采用贝叶斯技术，提供稳健的估计并允许丰富的参数化，并将它们应用于词汇化语法，从而提供语言语法属性的紧凑表示。这个CAREER项目还将培训研究生自然语言处理，并开发可用于教授初高中学生NLP的材料，并激励他们继续接受计算机科学教育。

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.