1. A method for resolving natural language ambiguities within text documents on a computer system comprising a processor and memory that would cause the processor to perform the following, comprising the steps of: i. training probabilistic classifiers from annotated training data containing a sense tag for each polysemous word; ii. processing said text documents into tokens and determining their part-of-speech tags; iii computing a measure of confidence using said probabilistic classifiers for each known sense of said tokens defined within a semantic lexicon based on contextual features and assigning a default sense for tokens absent from said semantic lexicon based on their part-of-speech tags; iv. determining assignment of word senses for each said token in said sentence such that the combined probability across said sentence is maximized; and v. integrating additional contextual features as generated by one or more of the following natural language processing modules into said probabilistic classifiers whereby said measure of confidence is improved: using a chunking module to identify multi-word phrases and the associated measure of confidence for each phrase; using a named-entity recognition module to identify named entities and the associated measure of confidence for each entity; using a syntactic parsing module to construct sentential parse trees and the associated measure of confidence for each tree; using an anaphora resolution module to identify anaphor references and the associated measure of confidence for each reference; using a discourse categorization module to determine document categories and the associated measure of confidence for each category; using a discourse structure analysis module to determine discourse structures and the associated measure of confidence for each structure.