CAREER: Statistical Learning Theory for Natural Language Processing: Theory, Algorithms and Representations

该项目的重点是在自然语言处理(NLP)的背景下进一步发展统计方法。该项目涉及三个核心理论或算法领域以及自然语言处理的三个应用领域的研究。核心领域是自然语言任务的监督学习；有效搜索和参数估计问题的研究，特别是通过探索加权上下文无关文法等NLP模型与图形模型中的搜索和估计工作之间的联系；以及最后，无监督或部分监督学习的研究。这三个应用领域是句法分析、自然语言界面学习和信息提取的学习方法。第一个目标是进一步发展对自然语言的机器学习方法的理论理解和新的算法。第二个目标是将这些算法应用于自然语言问题，特别是对导致这些任务良好性能的表示形式的理解。自然语言处理领域机器学习核心技术的改进可能会对广泛的自然语言处理应用产生影响。我们的目标是使在一个新的领域中建立一个自然语言界面或信息提取系统变得相对简单。我们对语言问题学习的理论理解的进步也可能对语言学、可学习性或语言习得的研究产生影响。最后，研究和教育计划的一个重要目标是在计算语言学和其他领域，特别是机器学习，以及人工智能中的概率表示和推理之间建立联系。

The focus of this project is the further development of statistical methods in the context of natural language processing (NLP). The project involves research in three core theoretical or algorithmic areas, and in three application areas within NLP. The core areas are supervised learning for natural language tasks; a study of the issues of efficient search and parameter estimation, particularly by exploring connections between NLP models such as weighted context-free grammars and work on search and estimation in graphical models; and finally, a study of unsupervised or partially supervised learning. The three application areas are syntactic analysis; learning in natural language interfaces; and learning approaches to information extraction.A first aim is to further develop a theoretical understanding, and new algorithms, for machine learning approaches to natural language. A second aim is to apply these algorithms to natural language problems, and in particular to develop an understanding of the representations which lead to good performance on these tasks. Improvements in core technologies for machine learning in natural language processing domains could have impact on a wide range of NLP applications. Our aim is to make it relatively simple to build a natural language interface, or information extraction system, in a new domain. Advances in our theoretical understanding of learning in language problems could also have an impact in linguistics, in studies of learnability or language acquisition. Finally, an important goal of the research and educational plan is to build connections between computational linguistics and other fields, particularly machine learning, and probabilistic representation and reasoning in artificial intelligence.