Symposium on Mathematical Statistics in Natural Language, San Francisco, CA, February 2001

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A symposium will be held at the 2001 meeting of the American Association for the Advancement of Science (AAAS) on the topic of statistical aspects of natural language analysis and processing.This symposium originates with the Association for the Mathematics of Language (MOL), a special interest group of the Association for Computational Linguistics which has held bi-annual meetings for over 10 years. The bigger goal of the proposed symposium is to heighten general scientific awareness of mathematical investigations of natural language structure and processing. Formal and computational studies of language draw on a broad range of mathematical fields (among them formal language theory; complexity; classical and non-classical logic; algebra and category theory; game theory) and have inspired innovations and extensions in a number of them. It would be difficult in a AAAS Symposium to address the full range of this work in a way that would do justice to both its breadth and its depth. The symposium will therefore concentrate on a single, more sharply delineated topic -- statistical aspects of natural language analysis -- in a way that connects technological advances in language engineering, linguistic structure and processing.Capacity for language depends on the interaction of biological endowment and empirical experience, in such a way that the stable interpretation of physical objects is possible in the face of broad variation across speakers, in changing contexts, and over time. Advances in computational methods of storage, search, and processing make it possible to model the dynamic interaction of endowment and experience qualitatively, in a way amenable to quantitative methods. This symposium focuses on approaches to this range of problems based on statistics and information theory. The presentations will begin with a survey of statistically-based work on speech processing (which underlies successful and available computational speech-to-text processing). It will then examine applications of these ideas to syntactic parsing and lexical ambiguity resolution. The presentations will be designed to stress the importance of the appropriateness of different statistical methodsto different problems in natural language analysis and processing and to show how distinctions among current models connect with such classical problems as the distinction between supervised and unsupervised learning.