Abstract: A method, computer program product, and computing system for generating a plurality of potential vocalizations of a plurality of text samples. A plurality of phonemes associated with the plurality of potential vocalizations are identified. A plurality of phonetically-related text portions are generated based upon, at least in part, the plurality of phonemes. A natural language understanding (NLU) engine is trained using the plurality of phonetically-related text portions.

Abstract: A system and method incorporate prior knowledge into the optimization and regularization of a classification and regression model. The optimization may be a regularization process and the prior knowledge may be incorporated through adjustment of a cost function. A method of at least one processor developing a classification and regression model may be provided. The method may be implemented by at least one processor that implements classification and regression model functionality, including receiving training data and adjusting the model according to the training data; testing the classification and regression model; and employing prior knowledge during an optimization of the classification and regression model. The regularizing can include adjusting feature weights according to prior knowledge. In various embodiments, such systems and methods can be used in the processing of language inputs, e.g., speech and/or text inputs, to achieve greater interpretation accuracy.

Abstract: A system and method incorporate prior knowledge into the optimization and regularization of a classification and regression model. The optimization may be a regularization process and the prior knowledge may be incorporated through adjustment of a cost function. A method of at least one processor developing a classification and regression model may be provided. The method may be implemented by at least one processor that implements classification and regression model functionality, including receiving training data and adjusting the model according to the training data; testing the classification and regression model; and employing prior knowledge during an optimization of the classification and regression model. The regularizing can include adjusting feature weights according to prior knowledge. In various embodiments, such systems and methods can be used in the processing of language inputs, e.g., speech and/or text inputs, to achieve greater interpretation accuracy.

Abstract: Aspects of the disclosure are directed to natural language processing or natural language understanding and may include a determination of a probabilistic or probability-based ranking of potential results. For example, natural language input may be received such as speech or text. Natural language processing may be performed to determine one or more potential results for the input. A pairwise classifier may be used to determine a score for element pairs in the potential results. Based on the scores, probabilities for the element pairs may be determined. Based on the probabilities for the element pairs, further probabilities may be determined such as by estimating the probability that a current result is the top rank or best choice. Based on the estimated probabilities that the current result is the top rank or best choice, a ranking may be determined, which may form the basis for natural language understanding output.

Abstract: Aspects of the disclosure are directed to natural language processing. An input interface of a computing device receives input (e.g., speech input) and generates a digital signal corresponding to that input. Text corresponding to the digital signal is obtained, and the text is processed using each of a context-free and a context-specific linguistic model to generate linguistic processing results for that text. The text and linguistic processing results may be processed using a NLU model to generate an NLU recognition result corresponding to the input received at the input interface. The text and the linguistic processing results may also be annotated and used to train a NLU model. The linguistic processing results may relate to, e.g., the tokenization of portions of the text, the normalization of portions of the text, sequences of normalizations for portions of the text, and rankings and prioritization of the linguistic processing results.

Abstract: Multiple natural language understanding (NLU) interpretation selection models may be generated. The NLU interpretation selection models may include a generic NLU interpretation selection model that is not specialized for a specific set of NLU interpretations type and one or more specialized NLU interpretation selection models, each of which may be specific to a particular set of NLU interpretations type. The specialized NLU interpretation selection model(s) may be utilized to process natural language input data comprising data corresponding to their respective sets of NLU interpretations type(s). The generic NLU interpretation selection model may be utilized to process natural language input data comprising data that does not correspond to the sets of NLU interpretations type(s) associated with the specialized NLU interpretation selection model(s).

Abstract: Aspects of the disclosure are directed to natural language processing. An input interface of a computing device receives input (e.g., speech input) and generates a digital signal corresponding to that input. Text corresponding to the digital signal is obtained, and the text is processed using each of a context-free and a context-specific linguistic model to generate linguistic processing results for that text. The text and linguistic processing results may be processed using a NLU model to generate an NLU recognition result corresponding to the input received at the input interface. The text and the linguistic processing results may also be annotated and used to train a NLU model. The linguistic processing results may relate to, e.g., the tokenization of portions of the text, the normalization of portions of the text, sequences of normalizations for portions of the text, and rankings and prioritization of the linguistic processing results.

Abstract: Aspects of the disclosure are directed to natural language processing. An input interface of a computing device receives input (e.g., speech input) and generates a digital signal corresponding to that input. Text corresponding to the digital signal is obtained, and the text is processed using each of a context-free and a context-specific linguistic model to generate linguistic processing results for that text. The text and linguistic processing results may be processed using a NLU model to generate an NLU recognition result corresponding to the input received at the input interface. The text and the linguistic processing results may also be annotated and used to train a NLU model. The linguistic processing results may relate to, e.g., the tokenization of portions of the text, the normalization of portions of the text, sequences of normalizations for portions of the text, and rankings and prioritization of the linguistic processing results.

Abstract: Multiple natural language understanding (NLU) interpretation selection models may be generated. The NLU interpretation selection models may include a generic NLU interpretation selection model that is not specialized for a specific set of NLU interpretations type and one or more specialized NLU interpretation selection models, each of which may be specific to a particular set of NLU interpretations type. The specialized NLU interpretation selection model(s) may be utilized to process natural language input data comprising data corresponding to their respective sets of NLU interpretations type(s). The generic NLU interpretation selection model may be utilized to process natural language input data comprising data that does not correspond to the sets of NLU interpretations type(s) associated with the specialized NLU interpretation selection model(s).

Abstract: The present invention provides imidazothiadiazole compounds of Formula (I) wherein A, B, D, Rx, R1, R2, R3, X1, X2 and s are as defined herein, or a stereoisomer, tautomer, pharmaceutically acceptable salt or solvate form thereof, wherein all of the variables are as defined herein. These compounds are inhibitors of platelet aggregation and thus can be used as medicaments.

Abstract: Multiple natural language understanding (NLU) interpretation selection models may be generated. The NLU interpretation selection models may include a generic NLU interpretation selection model that is not specialized for a specific set of NLU interpretations type and one or more specialized NLU interpretation selection models, each of which may be specific to a particular set of NLU interpretations type. The specialized NLU interpretation selection model(s) may be utilized to process natural language input data comprising data corresponding to their respective sets of NLU interpretations type(s). The generic NLU interpretation selection model may be utilized to process natural language input data comprising data that does not correspond to the sets of NLU interpretations type(s) associated with the specialized NLU interpretation selection model(s).

Abstract: Aspects of the disclosure are directed to natural language processing or natural language understanding and may include a determination of a probabilistic or probability-based ranking of potential results. For example, natural language input may be received such as speech or text. Natural language processing may be performed to determine one or more potential results for the input. A pairwise classifier may be used to determine a score for element pairs in the potential results. Based on the scores, probabilities for the element pairs may be determined. Based on the probabilities for the element pairs, further probabilities may be determined such as by estimating the probability that a current result is the top rank or best choice. Based on the estimated probabilities that the current result is the top rank or best choice, a ranking may be determined, which may form the basis for natural language understanding output.

Abstract: A natural language understanding system performs automatic unsupervised clustering of dialog data from a natural language dialog application. A log parser automatically extracts structured dialog data from application logs. A dialog generalizing module generalizes the extracted dialog data to generalization identifier vectors. A data clustering module automatically clusters the dialog data based on the generalization identifier vectors using an unsupervised density-based clustering algorithm without a predefined number of clusters and without a predefined distance threshold in an iterative approach based on a hierarchical ordering of the generalization.

Abstract: Disclosed methods and systems are directed to task switching in dialog processing. The methods and systems may include activating a primary task, receiving, one or more ambiguous natural language commands, and identifying a first candidate task for each of the one or more ambiguous natural language commands. The methods and system may also include identifying, for each of the one or more ambiguous natural language commands and based on one or more rules, a second candidate task of the plurality of tasks corresponding to the ambiguous natural language command, determining whether to modify at least one of the one or more rules-based task switching rules based on whether a quality metric satisfies a threshold quantity, and when the second quality metric satisfies the threshold quantity, changing the task switching rule for the corresponding candidate task from a rules-based model to the optimized statistical based task switching model.

Abstract: Aspects of the disclosure are directed to natural language processing. An input interface of a computing device receives input (e.g., speech input) and generates a digital signal corresponding to that input. Text corresponding to the digital signal is obtained, and the text is processed using each of a context-free and a context-specific linguistic model to generate linguistic processing results for that text. The text and linguistic processing results may be processed using a NLU model to generate an NLU recognition result corresponding to the input received at the input interface. The text and the linguistic processing results may also be annotated and used to train a NLU model. The linguistic processing results may relate to, e.g., the tokenization of portions of the text, the normalization of portions of the text, sequences of normalizations for portions of the text, and rankings and prioritization of the linguistic processing results.

Abstract: A method for refining a search is provided. Embodiments may include receiving a first speech signal corresponding to a first utterance and receiving a second speech signal corresponding to a second utterance, wherein the second utterance is a refinement to the first utterance. Embodiments may also include determining a first quantity of search results based upon the first speech signal information and determining a second quantity of search results based upon the second speech signal information. Embodiments may also include comparing at least one of the first quantity of search results and the second quantity of search results with a third quantity of search results and determining an information gain from the comparison.

Abstract: Disclosed methods and systems are directed to task switching in dialogue processing. The methods and systems may include activating a primary task, receiving, one or more ambiguous natural language commands, and identifying a first candidate task for each of the one or more ambiguous natural language commands. The methods and system may also include identifying, for each of the one or more ambiguous natural language commands and based on one or more rules, a second candidate task of the plurality of tasks corresponding to the ambiguous natural language command, determining whether to modify at least one of the one or more rules-based task switching rules based on whether a quality metric satisfies a threshold quantity, and when the second quality metric satisfies the threshold quantity, changing the task switching rule for the corresponding candidate task from a rules-based model to the optimized statistical based task switching model.

Abstract: A human-machine dialog system is described which has multiple computer-implemented dialog components. A user client delivers output prompts to a human user and receives dialog inputs from the human user including speech inputs. An automatic speech recognition (ASR) engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) engine processes the text words to determine corresponding NLU-ranked semantic interpretations. A semantic re-ranking module re-ranks the NLU-ranked semantic interpretations based on at least one of dialog context information and world knowledge information. A dialog manager responds to the re-ranked semantic interpretations and generates the output prompts so as to manage a dialog process with the human user.

Abstract: A method for refining a search is provided. Embodiments may include receiving a first speech signal corresponding to a first utterance and receiving a second speech signal corresponding to a second utterance, wherein the second utterance is a refinement to the first utterance. Embodiments may also include determining a first quantity of search results based upon the first speech signal information and determining a second quantity of search results based upon the second speech signal information. Embodiments may also include comparing at least one of the first quantity of search results and the second quantity of search results with a third quantity of search results and determining an information gain from the comparison.

Abstract: The present invention provides imidazothiadiazole compounds of Formula (I) wherein A, B, D, Rx, R1, R2, R3, X1, X2 and s are as defined herein, or a stereoisomer, tautomer, pharmaceutically acceptable salt or solvate form thereof, wherein all of the variables are as defined herein. These compounds are inhibitors of platelet aggregation and thus can be used as medicaments.