We have already referred to miscellaneous statistical models and techniques used in various computational tasks, such as (in section 2) HMMs in POS tagging, probabilistic grammar modeling and parsing, statistical semantics, semantic disambiguation (word senses, quantifier scope, etc.), plan recognition, discourse modeling, and knowledge extraction from text. Here we try to provide a brief, but slightly more systematic taxonomy of the types of tasks addressed in statistical NLP, and some sense of the modeling techniques and algorithms that are most commonly used and have made statistical NLP so predominant in recent years, challenging the traditional view of computational linguistics.