Microsoft Technology Licensing, LLC

Piali Choudhury

John Charles Krumm

William B Dolan

Yun-Cheng Ju

Aman Kansal

Arungunram Chandrasekaran Surendran

Christopher John Brockett

Raman Chandrasekar

Erin L Renshaw

Patrick Pantel

Michael K Forney

Margaret Ann Mitchell

Dean A Slawson

Melissa Nicole Lim

Dany Rouhana

Noelle Mallory Sophy

Svitlana Volkova

Ming-Wei Chang

Examples described herein provide a digital assistant crafting a response based on target sentiment identification from user input. The digital assistant receives unstructured data input and identifies a segment of the input that includes a facet item. A sentiment associated with the facet item in the segment is identified and classified to identify a targeted sentiment directed towards the facet item. A response is generated based on the targeted sentiment and the facet item.

Crafting a response based on sentiment identification

Different advantageous embodiments provide a crowdsourcing method for modeling user intent in conversational interfaces. One or more stimuli are presented to a plurality of describers. One or more sets of describer data are captured from the plurality of describers using a data collection mechanism. The one or more sets of describer data are processed to generate one or more models. Each of the one or more models is associated with a specific stimulus from the one or more stimuli.

Crowdsourced, grounded language for intent modeling in conversational interfaces

The subject disclosure is directed towards automatically labeling location-related information such as corresponding to GPS data or the like with a semantic label. A classifier trained with machine learning is provided with feature data corresponding to the location-related information and other features, such as user demographics data of a person associated with location-related information. The semantic label is received from the classifier, and associated with the location-related information. Other features may be used, such as other egocentric features corresponding to a person's particular visit, features from a sequence of visits, and/or features from other user information. The semantic label may be used to trigger an action, label a location on a map or the like, and so on.

Semantic place labels

An information dissemination system provides users information related to topics extracted from event classes. Event classes can generally be thought of as scripts that specify topics and actions for obtaining information related to the topics. The event classes are executed to obtain information related to the particular event class. The information dissemination system identifies a user event that is of interest to a user. The information dissemination system then identifies an event class that corresponds to the user event. The information dissemination system then performs the actions specified for the event class, obtains the action results, and renders the action results to the user.

Presenting information related to topics extracted from event classes

Growing community of inventors

Sammamish, WA, United States of America

Piali Choudhury