Abstract: A system is disclosed for determining probable meanings of a word. A prior probability is established of the probable meanings of the word. A context frequency probability is established of the probable meanings of the word. The probability that each meaning is a correct meaning may be provided in accordance with both the prior probability and the context frequency probability.

Abstract: A system is disclosed for expanding a user query based on user learned popularity rankings. User queries often have no exact match, resulting in a user having to refine or abandon the search. The disclosed system obtains popularity rankings based on the content of a user query. The system expands the user query based on the popularity rankings. Thereafter, query results based on the expanded user query may be provided to the user. The system also regularly learns from user behavior and adapts the popularity rankings according to the learned information.

Abstract: A system is disclosed for determining probable meanings of words. An input of a word is obtained. Probable meanings of the word may be determined in accordance with a prior probability of probable meanings of the word and a context frequency probability of probable meanings of the word.

Abstract: A system is disclosed for optimizing a user query. User queries often include issue terms, such as misspelled or mistyped terms. The disclosed system employs a fuzzy network to match an issue term with a valid term. The system optimizes the user query with the valid term. Thereafter, query results based on the optimized user query may be provided to the user.

Abstract: A system is disclosed for expanding a user query based on user learned popularity rankings. User queries often have no exact match, resulting in a user having to refine or abandon the search. The disclosed system obtains popularity rankings based on the content of a user query. The system expands the user query based on the popularity rankings. Thereafter, query results based on the expanded user query may be provided to the user. The system also regularly learns from user behavior and adapts the popularity rankings according to the learned information.

Abstract: A system is disclosed for determining probable meanings of a word. A prior probability is established of the probable meanings of the word. A context frequency probability is established of the probable meanings of the word. The probability that each meaning is a correct meaning may be provided in accordance with both the prior probability and the context frequency probability.

Abstract: A system is disclosed for determining probable meanings of words. An input of a word is obtained. Probable meanings of the word may be determined in accordance with a prior probability of probable meanings of the word and a context frequency probability of probable meanings of the word.