Abstract: A chart parser and a method for generating a parse chart for a sequence of input symbols in accordance with an abbreviated representation of a grammar. According to the method, an abbreviated representation of a grammar is stored as a set of finite-state automata, each finite-state automaton corresponding to a rule of the grammar. Chart edges are derived chart edges from the sequence of input symbols in accordance with the set of finite-state automata and are stored in the parse chart.

Abstract: A method for processing structured text is provided. Tokenized text is created from structured text in accordance with a predetermined set of tokenizer rules set forth in a token pattern knowledge base (13), in which each tokenizer rule defines a simplex constituent. Parsed text is created from tokenized text in accordance with a predetermined set of parser rules set forth in a parser rule knowledge base (15), in which each parser rule defines a complex constituent. Processed text is created from parsed text in accordance with a predetermined set of interpreter rules set forth in an interpretation knowledge base (17), in which each interpreter rule defines a message element corresponding to a simplex or complex constituent, whereby the processed text identifies and provides an interpretation of the message elements of the corresponding structured text for a useful purpose, such as text-to-speech synthesis.

Abstract: A chart parser and a method for generating a parse chart for a sequence of input symbols in accordance with an abbreviated representation of a grammar. According to the method, an abbreviated representation of a grammar is stored as a set of finite-state automata, each finite-state automaton corresponding to a rule of the grammar. Chart edges are derived chart edges from the sequence of input symbols in accordance with the set of finite-state automata and are stored in the parse chart.

Abstract: A method (900), device (200) and system (100) provide, in response to text/linguistic input, one of a set of pre-determined meanings which is the most likely intended meaning of that input. A trained meaning discriminator is generated from an annotated training corpus and a meaning discriminator trainer. The trained meaning discriminator generates a meaning vector from an input utterance. The intended meaning encoder analyzes the meaning vector to determine the most likely intended meaning and confidence measures.