Narrative Science Inc.

Salesforce, Inc.

Jared Lorince

Nathan Drew Nichols

Daniel Joseph Platt

Michael Justin Smathers

Disclosed herein is computer technology that provides adaptive mechanisms for learning concepts that are expressed by natural language sentences, and then applies this learning to appropriately classify new natural language sentences with the relevant concept that they express.

Applied artificial intelligence technology for adaptively classifying sentences based on the concepts they express to improve natural language understanding

Disclosed herein is computer technology that applies natural language processing (NLP) techniques to training data to generate information used to train a natural language generation (NLG) system to produce output that stylistically resembles the training data. In this fashion, the NLG system can be readily trained with training data supplied by a user so that the NLG system is adapted to produce output that stylistically resembles such training data. In an example, an NLP system detects a plurality of linguistic features in the training data. These detected linguistic features are then aggregated into a specification data structure that is arranged for training the NLG system to produce natural language output that stylistically resembles the training data. Parameters in the specification data structure can be linked to objects in an ontology used by the NLG system to facilitate the training of the NLG system based on the detected linguistic features.

Applied artificial intelligence technology for using natural language processing and concept expression templates to train a natural language generation system

Disclosed herein is computer technology that provides adaptive mechanisms for learning concepts that are expressed by natural language sentences, and then applies this learning to appropriately classify new natural language sentences with the relevant concept that they express. The computer technology can also discover the uniqueness of terms within a training corpus, and sufficiently unique terms can be flagged for the user for possible updates to an ontology for the system.

Applied artificial intelligence technology for adaptive natural language understanding with term discovery

Applied artificial intelligence technology for using natural language processing to train a natural language generation system with respect to date and number textual features

Applied artificial intelligence technology for using natural language processing to train a natural language generation system with respect to numeric style features

Applied artificial intelligence technology for using natural language processing to train a natural language generation system

Applied Artificial Intelligence Technology for Adaptive Natural Language Understanding Disclosed herein is computer technology that provides adaptive mechanisms for learning concepts that are expressed by natural language sentences, and then applies this learning to appropriately classify new natural language sentences with the relevant concept that they express.

Applied artificial intelligence technology for adaptive natural language understanding

Applied Artificial Intelligence Technology for Using Natural Language Processing and Concept Expression Templates To Train a Natural Language Generation System Disclosed herein is computer technology that applies natural language processing (NLP) techniques to training data to generate information used to train a natural language generation (NLG) system to produce output that stylistically resembles the training data. In this fashion, the NLG system can be readily trained with training data supplied by a user so that the NLG system is adapted to produce output that stylistically resembles such training data. In an example, an NLP system detects a plurality of linguistic features in the training data. These detected linguistic features are then aggregated into a specification data structure that is arranged for training the NLG system to produce natural language output that stylistically resembles the training data. Parameters in the specification data structure can be linked to objects in an ontology used by the NLG system to facilitate the training of the NLG system based on the detected linguistic features.

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Skokie, IL, United States of America

Jared Lorince