Abstract: Systems and methods are disclosed for predicting words using a structured stem and suffix n-gram language model. The systems and methods include determining, using a first n-gram word language model, a first probability of a stem based on a first portion of a previously-input word in the received input. Using a second n-gram language model, a second probability of a first suffix may be determined based at least on a second portion the previously-input word in the received input. Further, a third probability of a second suffix different from the first suffix may be determined using a third n-gram language model based at least on a third portion of the previously-input word in the received input. A fourth probability of a predicted word may be determined based on the first, second and third probabilities. One or more predicted words may be determined and provided as an output to the user.

Abstract: Systems and processes are disclosed for predicting words using a categorical stem and suffix word n-gram language model. A word prediction includes determining a stem probability using a stem language model. The word prediction also includes determining a suffix probability using suffix language model decoupled from the stem model, in view of one or more stem categories. The word prediction also includes determine a probability of the stem belonging to the stem category. A joint probability is determined based on the foregoing, and one or more word predictions having sufficient likelihood. In this way, the categorical stem and suffix language model constraints predicted suffixes to those that would be grammatically valid with predicted stems, thereby producing word predictions with grammatically valid stem and suffix combinations.

Abstract: Systems and methods are disclosed for predicting words using a structured stem and suffix n-gram language model. The systems and methods include determining, using a first n-gram word language model, a first probability of a stem based on a first portion of a previously-input word in the received input. Using a second n-gram language model, a second probability of a first suffix may be determined based at least on a second portion the previously-input word in the received input. Further, a third probability of a second suffix different from the first suffix may be determined using a third n-gram language model based at least on a third portion of the previously-input word in the received input. A fourth probability of a predicted word may be determined based on the first, second and third probabilities. One or more predicted words may be determined and provided as an output to the user.

Abstract: Systems and processes are disclosed for predicting words using a categorical stem and suffix word n-gram language model. A word prediction includes determining a stem probability using a stem language model. The word prediction also includes determining a suffix probability using suffix language model decoupled from the stem model, in view of one or more stem categories. The word prediction also includes determine a probability of the stem belonging to the stem category. A joint probability is determined based on the foregoing, and one or more word predictions having sufficient likelihood. In this way, the categorical stem and suffix language model constraints predicted suffixes to those that would be grammatically valid with predicted stems, thereby producing word predictions with grammatically valid stem and suffix combinations.

Abstract: A novel system integrates speech recognition and semantic classification, so that acoustic scores in a speech recognizer that accepts spoken utterances may be taken into account when training both language models and semantic classification models. For example, a joint association score may be defined that is indicative of a correspondence of a semantic class and a word sequence for an acoustic signal. The joint association score may incorporate parameters such as weighting parameters for signal-to-class modeling of the acoustic signal, language model parameters and scores, and acoustic model parameters and scores. The parameters may be revised to raise the joint association score of a target word sequence with a target semantic class relative to the joint association score of a competitor word sequence with the target semantic class. The parameters may be designed so that the semantic classification errors in the training data are minimized.

Abstract: Described is a technology in which product or service reviews are automatically processed to form a summary for each single product or service. Snippets from the reviews are extracted and classified into sentiment classes (e.g., as positive or negative) based on their wording. Attributes are assigned to the reviews, e.g., based on term frequency concepts, as nouns, which may be paired with adjectives and/or verbs. The summary of the reviews belonging to a single product or service is generated based on the automatically computed attributes and the classification of review snippets into attribute and sentiment classes. For example, the summary may indicate how many reviews were positive (the sentiment class), along with text corresponding to the most similar snippet based on its similarity to the attributes (the attribute class).

Abstract: A novel system integrates speech recognition and semantic classification, so that acoustic scores in a speech recognizer that accepts spoken utterances may be taken into account when training both language models and semantic classification models. For example, a joint association score may be defined that is indicative of a correspondence of a semantic class and a word sequence for an acoustic signal. The joint association score may incorporate parameters such as weighting parameters for signal-to-class modeling of the acoustic signal, language model parameters and scores, and acoustic model parameters and scores. The parameters may be revised to raise the joint association score of a target word sequence with a target semantic class relative to the joint association score of a competitor word sequence with the target semantic class. The parameters may be designed so that the semantic classification errors in the training data are minimized.