Amazon Technologies, Inc.

Miguel Ballesteros Martinez

Sunil Mallya Kasaragod

Yahor Pushkin

Yaser Al-Onaizan

Graham Vintcent Horwood

Yogarshi Paritosh Vyas

Sameer Karnik

Yinxiao Zhang

Rama Krishna Sandeep Pokkunuri

Yusheng Xie

Rishita Rajal Anubhai

Roger Scott Jenke

Saman Zarandioon

Atul Deo

Rajesh Goli

Abhinav Goyal

Ravindra Manjunatha

Bo Pang

Shuai Wang

Srikanth Doss Kadarundalagi Raghura

Kapil Singh Badesara

Jie Ma

Shinichi Kato

Aruni Roy Chowdhury

Liutong Zhou

Alessandra Brusadin

Xuan Zhu

Jonathan Steuck

Dan Roth

Diego Marcheggiani

Saurabh Giri

Yassine Benajiba

Raghavendraprasad Raghunath Prasad

Extraction of portions of natural language communications is performed to populate tables. An obtained communication may be associated with one, or more tables. The communication may include natural language data which may extracted and evaluated to predict different value mappings to the table. The value mappings may be confirmed or automatically made to the table.

Extracting data to populate tables from natural language communications

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

Methods, systems, and computer-readable media for event extraction from documents with co-reference are disclosed. An event extraction service identifies one or more trigger groups in a document comprising text. An individual one of the trigger groups comprises one or more textual references to an occurrence of an event. The one or more trigger groups are associated with one or more semantic roles for entities. The event extraction service identifies one or more entity groups in the document. An individual one of the entity groups comprises one or more textual references to a real-world object. The event extraction service assigns one or more of the entity groups to one or more of the semantic roles. The event extraction service generates an output indicating the one or more trigger groups and one or more entity groups assigned to the semantic roles.

Event extraction from documents with co-reference

Techniques for performing natural language processing (NLP) on semi-structured data are described. An exemplary method includes receiving a semi-structured document to perform NLP on using a trained NLP model; converting the semi-structured document into a secondary format, wherein the secondary format includes spatial information for tokens of the semi-structured document; flattening the converted, secondary formatted semi-structured document into a Unicode Transformation Format text file; performing NLP on the Unicode Transformation Format text file using the trained NLP model; and providing a result of the NLP to a requester.

Natural language processing on semi-structured data

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New York, NY, United States of America

Miguel Ballesteros Martinez