5. An extraction system for generating domain-specific grammar rules, the extraction system including a computer system having data processing logic configured to provide: a parser for parsing a plurality of documents stored in a digital document database on computer-accessible storage media to identify key terms of each document based on sentence structure; a feature extractor for: extracting a plurality of n-grams from each document, wherein one or more of the n-grams include spaces and partial words; extracting a frequency of each n-gram in each document; extracting a frequency of each n-gram in the plurality of documents; assigning a novelty score to each of the n-grams in corresponding documents, said novelty score representing and being based on the extracted frequency of the n-gram in the document and the extracted frequency of the n-gram in the plurality of documents, determining which of the extracted n-grams are in each identified key term, and assigning a weight to each key term based on the novelty scores assigned to the extracted n-grams in the key term; and a grammar generator for generating the domain-specific grammar rules for a speech recognition engine, said grammar rules including said key terms in association with respective probabilities based on the weights of the key terms, wherein the key terms define phrases that are likely to be spoken from the plurality of documents, and the grammar rules define which of the phrases are likely to follow others of the phrases with the likelihoods defined by the probabilities.
5. An extraction system for generating domain-specific grammar rules, the extraction system including a computer system having data processing logic configured to provide: a parser for parsing a plurality of documents stored in a digital document database on computer-accessible storage media to identify key terms of each document based on sentence structure; a feature extractor for: extracting a plurality of n-grams from each document, wherein one or more of the n-grams include spaces and partial words; extracting a frequency of each n-gram in each document; extracting a frequency of each n-gram in the plurality of documents; assigning a novelty score to each of the n-grams in corresponding documents, said novelty score representing and being based on the extracted frequency of the n-gram in the document and the extracted frequency of the n-gram in the plurality of documents, determining which of the extracted n-grams are in each identified key term, and assigning a weight to each key term based on the novelty scores assigned to the extracted n-grams in the key term; and a grammar generator for generating the domain-specific grammar rules for a speech recognition engine, said grammar rules including said key terms in association with respective probabilities based on the weights of the key terms, wherein the key terms define phrases that are likely to be spoken from the plurality of documents, and the grammar rules define which of the phrases are likely to follow others of the phrases with the likelihoods defined by the probabilities. 7. A system as claimed in claim 5 , wherein a natural language parser executes said parsing, and said key terms are linguistically important terms of each document.