Abstract: A method for executing a fully mixed initiative dialogue (FMID) interaction between a human and a machine, a dialogue system for a FMID interaction between a human and a machine and a computer readable data storage medium having stored thereon computer code for instructing a computer processor to execute a method for executing a FMID interaction between a human and a machine are provided. The method includes retrieving a predefined grammar setting out parameters for the interaction; receiving a voice input; analyzing the grammar to dynamically derive one or more semantic combinations based on the parameters; obtaining semantic content by performing voice recognition on the voice input; and assigning the semantic content as fulfilling the one or more semantic combinations.

Abstract: A computer-implemented method and apparatus defines a keyword class vector. A set of seed keywords is determined from a set of keywords and first and second most similar keywords from the set of seed keywords are then determined. A class vector is determined from first and second keyword vectors associated with the first and second most similar keywords. The method and apparatus also classifies a keyword in a keyword class. A similarity for a keyword vector associated with the keyword is determined with reference to a plurality of class vectors, each class vector having an associated class and determines a most similar class vector of the plurality of class vectors from the similarity determination. The keyword is then classified in a most similar class associated with the most similar class vector.

Abstract: A method for executing a fully mixed initiative dialogue (FMID) interaction between a human and a machine, a dialogue system for a FMID interaction between a human and a machine and a computer readable data storage medium having stored thereon computer code for instructing a computer processor to execute a method for executing a FMID interaction between a human and a machine are provided. The method includes retrieving a predefined grammar setting out parameters for the interaction; receiving a voice input; analysing the grammar to dynamically derive one or more semantic combinations based on the parameters; obtaining semantic content by performing voice recognition on the voice input; and assigning the semantic content as fulfilling the one or more semantic combinations.

Abstract: A system (100, 200) for tokenization and named entity recognition of ideographic language is disclosed. In the system, a word lattice is generated for a string of ideographic characters using finite state grammars (150) and a system lexicon (240). Segmented text is generated by determining word boundaries in the string of ideographic characters using the word lattice dependent upon a contextual language model (152A) and one or more entity language models (152B). One or more named entities is recognized in the string of ideographic characters using the word lattice dependent upon the contextual language model (152A) and the one or more entity language models (152B). The contextual language model (152A) and the one or more entity language models (152B) are each class-based language models. The lexicon (240) includes single ideographic characters, words, and predetermined features of the characters and words.