Rebecca M Sharp

US Patent:

20220358384, Nov 10, 2022

Filed:

May 4, 2022

Appl. No.:

17/736683

Inventors:

- Tucson AZ, US
Mihai Surdeanu - Tucson AZ, US
Sandeep Suntwal - Tucson AZ, US
Rebecca Sharp - Tucson AZ, US

International Classification:

G06N 5/04
G06N 5/02
G06N 3/08
G06N 3/04

Abstract:

A domain fact verification system is described having a computer programmed with a model trained using a process of data distillation and model distillation to improve model learning of the underlying semantics of a dataset rather than relying on statistical and lexical nuances in a domain-specific dataset. The computer thus programmed can accurately perform fact verification across multiple domains without the labor-intensive process of encoding a dataset of human-annotated, domain-specific information for each domain. Moreover, by combining data distillation with model distillation techniques, which may be seen as an inverse of well-established ensemble strategies (which train individual models separately and applies them jointly) the present domain transferable fact verification system scales better at inference time due to its reliance on a single trained model.

US Patent:

20210357585, Nov 18, 2021

Filed:

Jun 10, 2021

Appl. No.:

17/344774

Inventors:

- Tucson AZ, US
Marco A. Valenzuela Escarcega - Tucson AZ, US
Gustave Hahn-Powell - Tucson AZ, US
Dane Bell - Tucson AZ, US
Thomas Hicks - Tucson AZ, US
Enrique Noriega - Tucson AZ, US
Clayton Morrison - Tucson AZ, US
Rebecca Sharp - Tucson AZ, US
Robert Ionut Vacareanu - Botosani, RO
George Barbosa - Tucson AZ, US

International Classification:

G06F 40/211
G06F 40/284
G06F 40/169
G06F 40/253
G06F 40/295
G06F 3/0482
G06F 16/245

Abstract:

A machine reading system is described herein that includes a framework in which grammar rules can be developed using a concise language that combines syntax and semantics. The resulting technology thus reduces the development time for new grammars in a new domain. An enormous amount of information appears in the form of natural language across millions of academic papers and other literature sources. For example, in the biological domain, there is a tremendous ongoing effort to extract individual chemical interactions from these texts, but these interactions are only isolated fragments of larger causal mechanisms such as protein signaling pathways. The proposed rule-based event extraction framework can model underlying syntactic representations of events in order to extract signaling pathway fragments. Though application to the biomedical domain is herein described, the framework is domain-independent and is expressive enough to capture most complex events annotated by domain experts.