Google Inc.

Charmaine Cynthia Rose D'Silva

Anna Lynn Patterson

Advay Mengle

Jindong Chen

Daniel Andersson

Tania Bedrax Weiss

Md Sabbir Yousuf Sanny

Luke Friedman

Louis Shao

Methods and apparatus are described herein for classifying user reviews or portions thereof as being related to various entities, and for associating extracted descriptive segments of text contained in those user reviews or portions thereof with entities based on the classifications. In various implementations, one or more categories of observed user interest may be identified based on a corpus of user queries. One or more segments of text related to the one or more categories of observed user interest may be detected in one or more user reviews associated with a product. Based on the detecting, the product may be indexed on the one or more categories of observed user interest in a searchable database. In some implementations, the searchable database may be accessible to one or more remote client devices, and may be searchable by the one or more categories of observed user interest to provide search results to be rendered by the one or more remote client devices.

Analyzing user reviews to determine entity attributes

Methods and apparatus are described herein for classifying user reviews or portions thereof as being related to various entities, and for associating extracted descriptive segments of text contained in those user reviews or portions thereof with entities based on the classifications. In various implementations, one or more portions of a corpus of user reviews may be classified as being related to a first entity or a second entity. One or more descriptive segments of text may be extracted from the one or more classified portions. The one or more extracted descriptive segments of text may be associated with the first or second entity based on classifications of the one or more classified portions.

Methods and apparatus are disclosed for determining similarities and/or differences between entities in a segment of text based on various signals are presented, and for determining one or more likelihoods that one or more subjects found in a segment of text are capable of performing one or more associated actions based on various signals.

Automated reading comprehension

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Sunnyvale, CA, United States of America

Charmaine Cynthia Rose D'Silva