Abstract: A multi-pass method for processing text for use with a natural language understanding system can include a series of steps. The steps can include determining at least one contextual marker in the text and identifying a referrent in a question in the text. In a separate referrent mapping pass through the text, the method can include classifying the identified referrent as a particular type of referrent using the contextual marker and the identified referrent.

Abstract: A speech coding apparatus and method uses classification rules to code an utterance while consuming fewer computing resources. The value of at least one feature of an utterance is measured during each of a series of successive time intervals to produce a series of feature vector signals representing the feature values. The classification rules comprise at least first and second sets of classification rules. The first set of classification rules map each feature vector signal from a set of all possible feature vector signals to exactly one of at least two disjoint subsets of feature vector signals. The second set of classification rules map each feature vector signal in a subset of feature vector signals to exactly one of at least two different classes of prototype vector signals. Each class contains a plurality of prototype vector signals. According to the classification rules, a first feature vector signal is mapped to a first class of prototype vector signals.

Abstract: An aqueous cleaning composition useful for cleaning hard surfaces such as glass, and a method for utilizing same, wherein relatively no residue is formed by the composition upon application to the surface. The composition comprises an aqueous mixture of an alcohol, a glycol ether, and a surfactant, wherein the surfactant is a fluorosurfactant present in an amount equal to or less than 40 parts per million, preferably 30 parts per million, based on the total composition.

Abstract: A method and apparatus for modeling words based on match scores representing (a) the closeness of a match between probabilistic word models and the acoustic features of at least two utterances, and (b) the closeness of a match between word models and the spelling of the word. A match score is calculated for a selection set of one or more probabilistic word models. A match score is also calculated for an expansion set comprising the probabilistic word models in the selection set and one probabilistic word model from a candidate set. If the expansion set match score improves the selection set match score by a selected nonzero threshold value, the word is modelled with the word models in the expansion set. If the expansion set match score does not improve the selection set match score by the selected nonzero threshold value, the word is modelled with the words in the selection set.