Abstract: The present disclosure provides a computer system, method, and computer-readable medium for a computer processor to determine whether to report a consumer message regarding a vehicle to a regulatory agency. The computer system receives a message from a consumer. The computer system applies the message to a machine learning classifier trained to determine whether a category of vehicle system is described in the message, the machine learning classifier trained using category definitions and corresponding parts and symptoms labeled with the category definitions. The computer system determines whether the message includes a complaint based on an ontology defining a vehicle problem lexicon, a car part lexicon, and lexical patterns. The computer system extracts MVS terms from the message. The computer system determines to report the message if the message includes a complaint related to at least one category of vehicle system and includes a set of the MVS terms.

Abstract: The present disclosure provides a computer system, method, and computer-readable medium for a computer processor to determine whether to report a consumer message regarding a vehicle to a regulatory agency. The computer system receives a message from a consumer. The computer system applies the message to a machine learning classifier trained to determine whether a category of vehicle system is described in the message, the machine learning classifier trained using category definitions and corresponding parts and symptoms labeled with the category definitions. The computer system determines whether the message includes a complaint based on an ontology defining a vehicle problem lexicon, a car part lexicon, and lexical patterns. The computer system extracts MVS terms from the message. The computer system determines to report the message if the message includes a complaint related to at least one category of vehicle system and includes a set of the MVS terms.

Abstract: There is disclosed a system and method for automatically performing semantic categorization. In one embodiment at least one text description pertaining to a category set is accepted along with words that are anticipated to be uttered by a user pertaining to that category set; lexical chaining confidence score is attached to each pair matched between the anticipated words and the accepted text description. These confidence scores are used subsequently by a categorization circuit that accepts a text phrase utterance from an input source along with a category set pertaining to the accepted utterance. The categorization circuit, in one embodiment, creates word pairs matched between the accepted text phrase utterance and the accepted category set. From these word scores, the category pertaining to the utterance is determined based, at least in part, on the assigned lexical chaining confidence scores as previously determined.

Abstract: There is disclosed a system and method for automatically performing semantic categorization. In one embodiment at least one text description pertaining to a category set is accepted along with words that are anticipated to be uttered by a user pertaining to that category set; lexical chaining confidence score is attached to each pair matched between the anticipated words and the accepted text description. These confidence scores are used subsequently by a categorization circuit that accepts a text phrase utterance from an input source along with a category set pertaining to the accepted utterance. The categorization circuit, in one embodiment, creates word pairs matched between the accepted text phrase utterance and the accepted category set. From these word scores, the category pertaining to the utterance is determined based, at least in part, on the assigned lexical chaining confidence scores as previously determined.

Abstract: A Statistical Language Model (SLM) that can be used in an ASR for Interactive Voice Response (IVR) systems in general and Natural Language Speech Applications (NLSAs) in particular can be created by first manually producing a brief description in text for each task that can be performed in an NLSA. These brief descriptions are then analyzed, in one embodiment, to generate spontaneous speech utterances based pre-filler patterns and a skeletal set of content words. The pre-filler patterns are in turn used with Part-of-Speech (POS) tagged conversations from a spontaneous speech corpus to generate a set of pre-filler phrases. The skeletal set of content words is used with an electronic lexico-semantic database and with a thesaurus-based content word extraction process to generate a more extensive list of content words. The pre-filler phrases and content words set, thus generated, are combined into utterances using a lexico-semantic resource based process.