Nuance Communications, Inc.

Leonid Rachevsky

Bhuvana Ramabhadran

Dimitri Kanevsky

Raimo Bakis

Disclosed methods and systems are directed to generating ontological relationships. The methods and systems may include receiving a set of words comprising one or more verbs and a plurality of nouns and determining one or more first ontological relationships between the plurality of nouns based on an association of each of the nouns with at least one of the one or more verbs; and a correspondence between one or more glosses associated with each of the plurality of nouns. The methods and systems may include receiving an input associated with the one or more first ontological relationships, and determining, based on the input, one or more second ontological relationships between the plurality of nouns.

Iterative ontology discovery

Aspects described herein provide various approaches to annotating text samples in order to construct natural language grammars. A text sample may be selected for annotation. A set of annotation candidates may be generated based on the text sample. A classifier may be used to score the set of annotation candidates in order to obtain a set of annotation scores. One of the annotation candidates may be selected as a suggested annotation for the text sample based on the set of annotation scores. A grammar rule may be derived based on the suggested annotation, and a grammar may be configured to include the annotation-derived grammar rule.

Automated text annotation for construction of natural language understanding grammars

Some aspects include transforming data, at least a portion of which has been processed to determine at least one representative vector associated with each of a plurality of classifications associated with the data to obtain a plurality of representative vectors. Techniques comprise determining a first transformation based, at least in part, on the plurality of representative vectors, applying at least the first transformation to the data to obtain transformed data, and fitting a plurality of clusters to the transformed data to obtain a plurality of established clusters. Some aspects include classifying input data by transforming the input data using at least the first transformation and comparing the transformed input data to the established clusters.

Methods and apparatus for performing transformation techniques for data clustering and/or classification

Some aspects include transforming data, at least a portion of which has been processed to determine frequency information associated with features in the data. Techniques include determining a first transformation based, at least in part, on the frequency information, applying at least the first transformation to the data to obtain transformed data, and fitting a plurality of clusters to the transformed data to obtain a plurality of established clusters. Some aspects include classifying input data by transforming the input data using at least the first transformation and comparing the transformed input data to the established clusters.

Some aspects include transforming data for which at least one constraint has been specified on a portion of the data, the at least one constraint relating to a similarity and/or dissimilarity of at least some of the portion of the data. Techniques comprise determining a first transformation that approximates the at least one constraint using a cosine similarity as a measure of the similarity and/or dissimilarity of the at least a portion of the data, applying at least the first transformation to the data to obtain transformed data, and fitting a plurality of clusters to the transformed data to obtain a plurality of established clusters. Some aspects include classifying input data by transforming the input data using at least the first transformation and comparing the transformed input data to the established clusters.

Growing community of inventors

Ossining, NY, United States of America

Leonid Rachevsky