The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 09, 2021

Filed:

Mar. 29, 2019
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Hoifung Poon, Bellevue, WA (US);

Cliff Wong, Seattle, WA (US);

Robin Jia, Stanford, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/284 (2020.01); G06N 20/20 (2019.01); G06N 20/10 (2019.01); G06F 40/205 (2020.01); G06F 40/30 (2020.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06F 40/295 (2020.01);
U.S. Cl.
CPC ...
G06F 40/284 (2020.01); G06F 40/205 (2020.01); G06F 40/295 (2020.01); G06F 40/30 (2020.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01);
Abstract

A computing system is provided. The computing system includes a processor configured to execute one or more programs and associated memory. The processor is configured to execute neural network system that includes a first neural network and a second neural network. The processor is configured to receive input text, and for each of a plurality of text spans within the input text: identify a vector of semantic entities and a vector of entity mentions; define an n-ary relation between entity mentions including subrelations; and determine mention-level representation vectors in the text spans that satisfy the n-ary relation or subrelations. The processor is configured to: aggregate the mention-level representation vectors over all of the text spans to produce entity-level representation vectors; input to the second neural network the entity-level representation vectors; and output a prediction of a presence of the n-ary relation for the semantic entities in the input text.


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