Narrative Science Inc.

Salesforce, Inc.

Michael Tien Thinh Pham

Nathan Drew Nichols

Andrew R Paley

Maia Lewis Meza

Matthew Lloyd Trahan

Charlie M Truong

Nathan William Krapf

Clayton Nicholas Norris

Stephen Emmanuel Hudson

Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. Narrative analytics that are linked to communication goal statements can employ a conditional outcome framework that allows the content and structure of resulting narratives to intelligently adapt as a function of the nature of the data under consideration. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired communication goal.

Applied artificial intelligence technology for narrative generation based on a conditional outcome framework

NLP techniques are disclosed that apply computer technology to sentence data for performing entity referencing. For example, a processor can parse sentence data in a defined window of sentence data into a list of entity terms and a plurality of classifications associated with the listed entity terms. A processor can also a plurality of context saliency scores for a plurality of the listed entity terms based on the classifications associated with the listed entity terms as well as maintain a list of referring terms corresponding to the listed entity terms. For new sentence data that includes a referring term from the referring term list, a processor can (i) select a corresponding entity term on the entity term list based on the context saliency scores for the entity terms, and (ii) infer that the referring term in the new sentence data refers to the selected corresponding entity term.

Context saliency-based deictic parser for natural language processing

NLG techniques are disclosed that apply computer technology to sentence data for performing entity referencing. For example, a processor can parse sentence data in a defined window of sentence data into a list of entity terms and a plurality of classifications associated with the listed entity terms. A processor can also compute a plurality of context saliency scores for a plurality of the listed entity terms based on the classifications associated with the listed entity terms. For new sentence data that refers to an entity term from the entity term list, a processor can select a referring term for referencing that entity term from a list of candidate referring terms based on the context saliency scores for the entity terms. A processor can then form the new sentence data such that the new sentence data includes the selected referring term to refer to the at least one entity term.

Context saliency-based deictic parser for natural language generation

Context Saliency-Based Deictic Parser for Natural Language Processing NLP techniques are disclosed that apply computer technology to sentence data for performing entity referencing. For example, a processor can parse sentence data in a defined window of sentence data into a list of entity terms and a plurality of classifications associated with the listed entity terms. A processor can also a plurality of context saliency scores for a plurality of the listed entity terms based on the classifications associated with the listed entity terms as well as maintain a list of referring terms corresponding to the listed entity terms. For new sentence data that includes a referring term from the referring term list, a processor can (i) select a corresponding entity term on the entity term list based on the context saliency scores for the entity terms, and (ii) infer that the referring term in the new sentence data refers to the selected corresponding entity term.

Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines using 'analyze' communication goals in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired analysis communication goal such that the narratives will express various ideas that are deemed relevant to a given analysis communication goal.

Growing community of inventors

Chicago, IL, United States of America

Michael Tien Thinh Pham