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:
Jul. 28, 2020

Filed:

Nov. 17, 2017
Applicant:

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

Inventors:

Man Chu, Brooklyn, NY (US);

Steven M. Pritko, Pittsburg, PA (US);

Zhe Zhang, Cary, NC (US);

Justin A. Ziniel, Columbus, OH (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/35 (2019.01); G06F 16/332 (2019.01); G06F 16/36 (2019.01); G06F 16/2457 (2019.01); G06N 5/04 (2006.01); G10L 15/22 (2006.01); G10L 15/18 (2013.01); G06N 3/00 (2006.01); G06N 20/00 (2019.01); G06F 40/30 (2020.01); G06F 40/35 (2020.01); G06F 40/117 (2020.01);
U.S. Cl.
CPC ...
G06F 16/358 (2019.01); G06F 40/117 (2020.01); G06F 40/30 (2020.01); G06F 40/35 (2020.01); G06N 3/006 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G10L 15/1815 (2013.01); G10L 15/22 (2013.01); G06F 16/24578 (2019.01); G06F 16/3329 (2019.01); G06F 16/353 (2019.01); G06F 16/367 (2019.01);
Abstract

Software that selects portions of unlabeled text for labeling, by performing the following operations: (i) receiving a set of unlabeled input text for classification with respect to a particular domain, wherein the domain includes a labeled corpus for which topics of a set of topics correspond to labels from the corpus, and wherein the topics include statistical probability distributions of words in the corpus; (ii) performing topic modeling on the input text to associate portions of the input text with respective classifications, wherein the classifications include statistical probability distributions of topics of the set of topics in the respective portions of the input text; and (iii) applying a machine learning-based selection strategy to the portions of the input text and their respective classifications to identify one or more portions of the input text for labeling.


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