MSPA-MCS: Collaborative Research: Statistical Learning Methods for Complex Decision Problems in Natural Language Processing

MSPA-MCS：协作研究：自然语言处理中复杂决策问题的统计学习方法

• 批准号: 0434383
• 负责人: Peter Bartlett
• 金额: $ 31.81万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0434383&HistoricalAwards=false
• 关键词: MSPA; MCS; Collaborative; Research; Statistical

Pattern classification problems that arise in natural language processing applications, such as parsing, machine translation, and speech recognition, are more complex than those commonly addressed with statistical learning methods. The broad goal of this research project is the design and analysis of statistical learning algorithms that are suitable for these problems. The research is focused on the following questions, which are motivated by characteristic properties of complex pattern classification problems in natural language processing: methods for multiclass classification with desirable statistical and computational properties; methods for structured classification, where the predicted variables come from a large set with a rich structure (for example, predicting the parse tree of a sentence); the extension of these methods to problems with hidden variables, that is, where some relevant data is not observed; and complex nonparametric models for these problems, in particular, computationally efficient nonparametric Bayesian methods based on hierarchical Dirichlet processes. The methods developed will be validated empirically on parsing, machine translation, and speech recognition problems.The research project is aimed at the development and analysis of statistical learning methods for complex decision problems, such as those that arise in natural language processing. A key goal of research in natural language processing is the development of automated systems, such as translation systems and dialogue systems. The most successful approaches involve the use of statistical methods to exploit language data, such as a text corpus. However, the decision problems that arise are very complex. A good example is the problem of parsing, or recovering the syntactic structure underlying sentences in a language. For such problems, the set of candidate decisions is very large, and possesses considerable structure. This research project is aimed at developing computational and statistical methods that are suitable for complex decision problems of this kind. Successful methods are also likely to have a significant impact in other areas of computer science, including computer vision and bioinformatics, because similar complex decision problems also arise in these areas.

自然语言处理应用（例如解析、机器翻译和语音识别）中出现的模式分类问题比统计学习方法通​​常解决的问题更为复杂。 该研究项目的总体目标是设计和分析适合这些问题的统计学习算法。 该研究的重点是以下问题，这些问题是由自然语言处理中复杂模式分类问题的特征驱动的：具有理想统计和计算特性的多类分类方法；结构化分类方法，其中预测变量来自具有丰富结构的大集合（例如，预测句子的解析树）；将这些方法扩展到具有隐藏变量的问题，即未观察到一些相关数据的问题；以及针对这些问题的复杂非参数模型，特别是基于分层狄利克雷过程的计算高效的非参数贝叶斯方法。 所开发的方法将在解析、机器翻译和语音识别问题上进行实证验证。该研究项目旨在开发和分析复杂决策问题的统计学习方法，例如自然语言处理中出现的问题。 自然语言处理研究的一个关键目标是开发自动化系统，例如翻译系统和对话系统。 最成功的方法涉及使用统计方法来利用语言数据，例如文本语料库。 然而，出现的决策问题非常复杂。 一个很好的例子是解析或恢复语言中句子背后的句法结构的问题。 对于此类问题，候选决策集非常大，并且具有相当大的结构。 该研究项目旨在开发适用于此类复杂决策问题的计算和统计方法。 成功的方法也可能对计算机科学的其他领域产生重大影响，包括计算机视觉和生物信息学，因为这些领域也出现类似的复杂决策问题。