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:
May. 16, 2023

Filed:

Feb. 13, 2020
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Avishai Gretz, Ramat-Gan, IL;

Edo Cohen-Karlik, Tel-Aviv, IL;

Noam Slonim, Jerusalem, IL;

Assaf Toledo, Ramat-Gan, IL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/284 (2020.01); G06N 7/00 (2006.01); G06N 20/00 (2019.01); G06N 5/04 (2006.01); G10L 15/16 (2006.01); G10L 15/183 (2013.01); G06N 5/045 (2023.01); G06N 3/04 (2023.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/284 (2020.01); G06N 5/045 (2013.01); G06N 7/005 (2013.01); G06N 20/00 (2019.01); G10L 15/16 (2013.01); G10L 15/183 (2013.01); G06N 3/0454 (2013.01);
Abstract

Automated detection of reasoning in arguments. A training set is generated by: obtaining multiple arguments, each comprising one or more sentences provided as digital text; automatically estimating a probability that each of the arguments includes reasoning, wherein the estimating comprises applying a contextual language model to each of the arguments; automatically labeling as positive examples those of the arguments which have a relatively high probability to include reasoning; and automatically labeling as negative examples those of the arguments which have a relatively low probability to include reasoning. Based on the generated training set, a machine learning classifier is automatically trained to estimate a probability that a new argument includes reasoning. The trained machine learning classifier is applied to the new argument, to estimate a probability that the new argument includes reasoning.


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