MSPA-MCS: collaborative research: Statistical Learning Methods for Complex Decision Problems in Natural Language Processing

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

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.