Abstract: A method and apparatus for extracting multi-word technical terms from a text file in a computer system. Word strings are selected from the text that have at least two words, that have at most a specified maximum number of words, that include none of a special set of selected tokens, and that only include selected characters. Word string which occur less than a specified minimum number of times in the text file are deleted. The remaining strings form a set of word strings very likely to be multi-word technical terms. Improvements on the quality of the set of word strings can be accomplished by deleting word strings which do not satisfy certain grammatical constraints.

Abstract: Apparatus and method for evaluating the likelihood of an event (such as a word) following a string of known events, based on event sequence counts derived from sparse sample data. Event sequences--or m-grams--include a key and a subsequent event. For each m-gram is stored a discounted probability generated by applying modified Turing's estimate, for example, to a count-based probability. For a key occurring in the sample data there is stored a normalization constant which preferably (a) adjusts the discounted probabilities for multiple counting, if any, and (b) includes a freed probability mass allocated to m-grams which do not occur in the sample data. To determine the likelihood of a selected event following a string of known events, a "backing off" scheme is employed in which successively shorter keys (of known events) followed by the selected event (representing m-grams) are searched until an m-gram is found having a discounted probability stored therefor.