Yee H Chan

US Patent:

20220180073, Jun 9, 2022

Filed:

Dec 16, 2021

Appl. No.:

17/553312

Inventors:

- Cambridge MA, US
Yee Seng Chan - Winchester MA, US
Ilana Heintz - Silver Spring MD, US

International Classification:

G06F 40/58
G06N 20/00
G06F 40/253
G06F 40/30
G06N 3/04

Abstract:

A machine accesses a preexisting set of natural language text documents in multiple natural languages. Each natural language text document in at least a portion of the preexisting set is associated with an event. The machine trains, using the preexisting set of natural language text documents and the associated events, an event encoder to learn associations between texts and event annotations. The event encoder leverages a parser in each of the two or more natural languages. The machine generates, using the event encoder, new event annotations for texts. The machine trains, using the preexisting set of natural language text documents and the new event annotations for the texts generated by the event encoder, an event extraction engine to extract events from natural language texts in the two or more natural languages. The event extraction engine leverages the parser in each of the two or more natural languages.

US Patent:

20200387574, Dec 10, 2020

Filed:

Jun 7, 2019

Appl. No.:

16/434710

Inventors:

- Cambridge MA, US
Yee Seng Chan - Winchester MA, US
Ilana Heintz - Silver Spring MD, US

International Classification:

G06F 17/28
G06F 17/27
G06N 3/04
G06N 20/00

Abstract:

A machine accesses a preexisting set of natural language text documents in multiple natural languages. Each natural language text document in at least a portion of the preexisting set is associated with an event. The machine trains, using the preexisting set of natural language text documents and the associated events, an event encoder to learn associations between texts and event annotations. The event encoder leverages a parser in each of the two or more natural languages. The machine generates, using the event encoder, new event annotations for texts. The machine trains, using the preexisting set of natural language text documents and the new event annotations for the texts generated by the event encoder, an event extraction engine to extract events from natural language texts in the two or more natural languages. The event extraction engine leverages the parser in each of the two or more natural languages.