Amazon Technologies, Inc.

Abhinav Goyal

Sunil Mallya Kasaragod

Yahor Pushkin

Yaser Al-Onaizan

Sameer Karnik

Srikanth Doss Kadarundalagi Raghura

Sravan Babu Bodapati

Rishita Rajal Anubhai

Miguel Ballesteros Martinez

Kalpit Dixit

Yogarshi Paritosh Vyas

Graham Vintcent Horwood

Kasturi Bhattacharjee

Syed Kashif Hussain Shah

Kapil Singh Badesara

Smaranda Muresan

Ravindra Manjunatha

Federico Torreti

Siddharth Chaitanyakumar Varia

Alok Kumar Parmesh

Methods, systems, and computer-readable media for context-sensitive linking of entities to private databases are disclosed. An entity linking service stores a plurality of representations of entities. Individual ones of the entities correspond to individual ones of a plurality of records in one or more private databases. The entity linking service determines a mention of an entity in a document. The entity linking service selects, from the plurality of records in the one or more private databases, a record corresponding to the entity. The record is selected based at least in part on the plurality of representations of the entities and based at least in part on a context of the mention of the entity in the document. The entity linking service generates output comprising a reference to the selected record in the one or more private databases.

Context-sensitive linking of entities to private databases

A global segmenting and analysis service of a provider network may receive documents (e.g., posts, product reviews) from different applications. The service may analyze the documents to identify target entities and sentiment. The service may generate different levels of sentiment data and store data into a segmented database. For example, the service may store within-document level sentiment, document-level sentiment, and multi-document level sentiment for a target entity. The service may also update the entity taxonomy automatically or with only a small number of sample documents. The client may query the service for the segmented sentiment data.

Global segmenting and sentiment analysis based on granular opinion detection

Various embodiments of a hierarchical system or method of identifying sensitive content in data is described. In some embodiments, sensitive data classifiers local to a data storage system can analyze a plurality of data items and classify at least some data items as potentially containing sensitive data. The sensitive data classifiers can provide the classified data items to a separate sensitive data discovery component. The sensitive data discovery component can, in some embodiments, obtain the classified data items, perform a sensitive data location analysis on the classified data items to identify a location of sensitive data within some of the classified data items, and generate location information for the sensitive data within the data items containing sensitive data. The sensitive data discovery component can provide to a destination this information, in some embodiments, where the destination might redact, tokenize, highlight, or perform other actions on the located sensitive data.

Hierarchical system and method for identifying sensitive content in data

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Snohomish, WA, United States of America

Abhinav Goyal