10. A method comprising: under control of one or more processors configured with executable instructions, receiving a partial query input comprising voice signals of a user and at least one of text, image or audio input by a user; inferring in real-time, using a classifier, multiple different search goals based on the partial query input, the classifier comprising a support vector machine to find a hyperspace in a space of possible inputs to distinguish triggering input events from non-triggering input events, the classifier explicitly trained using generic training data and implicitly trained by observing user behavior; deriving similar or matching character sets, terms, or phrases to the partial query input by accessing one or more query databases that store query-related information; formulating at least one complete query based on the multiple different search goals; presenting the at least one complete query to the user for editing; processing the at least one complete query to return search results; assigning search results to each of the possible interpretations for selection by the user; outputting a confidence value associated with each of the multiple different search goals; formulating a complete query for each of the multiple different search goals; executing each complete query to return search results; and adjusting a number of the search results to be associated with each of the formal queries based at least in part on the associated confidence value.
10. A method comprising: under control of one or more processors configured with executable instructions, receiving a partial query input comprising voice signals of a user and at least one of text, image or audio input by a user; inferring in real-time, using a classifier, multiple different search goals based on the partial query input, the classifier comprising a support vector machine to find a hyperspace in a space of possible inputs to distinguish triggering input events from non-triggering input events, the classifier explicitly trained using generic training data and implicitly trained by observing user behavior; deriving similar or matching character sets, terms, or phrases to the partial query input by accessing one or more query databases that store query-related information; formulating at least one complete query based on the multiple different search goals; presenting the at least one complete query to the user for editing; processing the at least one complete query to return search results; assigning search results to each of the possible interpretations for selection by the user; outputting a confidence value associated with each of the multiple different search goals; formulating a complete query for each of the multiple different search goals; executing each complete query to return search results; and adjusting a number of the search results to be associated with each of the formal queries based at least in part on the associated confidence value. 13. The method of claim 10 , further comprising performing a search based on the at least one complete query, and storing ambiguities derived as part of the search for processing of a future search.