Ask Ziggy, Inc.

Kyle Wade Grove

Shai Leib

Thomas Bertran

Nirmalya Kumar De

Dipesh Gandhi

A method for generating an natural language processing (NLP) model including obtaining tags, obtaining actions to be implemented by a third party application, obtaining a training corpus including sentences, where at least one word in each of the sentences is associated with one of the tags, and wherein each of the sentences is associated with one of the actions. The method further includes generating features for the NLP model for the third party application using the tags, the actions, and the training corpus, training the NLP model using the features and the training corpus to obtain a trained NLP model, and generating an APIKey for use by the third party application, where the API provides the third party application access to the trained NLP model.

Natural language processing (NLP) portal for third party applications

A method for processing transcriptions using natural language processing (NLP), the method includes obtaining transcriptions corresponding to an utterance from a user device, where each of the transcriptions is a different speech-to-text conversion of the utterance. The method further includes tagging a first transcription with at least one entity tag and a first transcription-level tag to obtain a first tagged transcription, tagging the second transcription with at least one entity tag and a second transcription-level tag to obtain a second tagged transcription, determining a highest probability transcription-level tag from the first transcription-level tag and second transcription-level tag. The method further includes identifying a subject-matter domain using the highest probability transcription-level tag, retagging the first transcription and the second transcription using entity tags associated with the subject-matter domain to obtain retagged transcriptions, performing, using the retagged transcriptions, an action to obtain a result, and sending the result to the user device.

Method and system for domain-specific noisy channel natural language processing (NLP)

A method and system for performing a task in a natural language processing system. The method includes processing text to obtain a numerical encoding, where the text includes a number of entities, where at least one of the entities is tagged with the numerical encoding, and where the text is derived from an utterance. The method further includes determining that the numerical encoding identifies a task, determining a variable required to complete the task using the numerical encoding, and generating a first dialogue sentence to obtain a variable value for the variable, where the variable is associated with a second numerical encoding, where the second numerical encoding includes the numerical encoding. The method further includes transmitting the first dialogue sentence to a user device, receiving the variable value for the variable in response to the first dialogue sentence, and performing the task using the variable value.

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Roseville, CA, United States of America

Kyle Wade Grove