Statistical Natural Language Processing

Abstract

The application of statistical methods to natural language processing has been remarkably successful over the past two decades. The wide availability of text and speech corpora has played a critical role in their success since, as for all learning techniques, these methods rely heavily on data. Many of the components of complex natural language processing systems, for example, text normalizers, morphological or phonological analyzers, part-of-speech taggers, grammars or language models, pronunciation models, context-dependency models, acoustic Hidden-Markov Models (HMMs), are statistical models derived from large data sets using modern learning techniques. These models are often given as weighted automata or weighted finite-state transducers either directly or as a result of the approximation of more complex models.