Invention Grant
- Patent Title: Classification of sparsely labeled text documents while preserving semantics
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Application No.: US16441927Application Date: 2019-06-14
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Publication No.: US11455527B2Publication Date: 2022-09-27
- Inventor: John J. Thomas , Aleksandr E. Petrov , Wanting Wang , Maxime Allard
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Otterstedt & Kammer PLLC
- Agent Jorge Maranto
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06F17/15 ; G06N3/04 ; G06F40/30

Abstract:
A method of training a neural network includes receiving a text corpus containing a labeled portion and an unlabeled portion, extracting local n-gram features and a sequence of the local n-gram features from the text corpus, processing the text corpus, using convolutional layers, according to the local n-gram features to determine capsule parameters of capsules configured to preserve the sequence of the local n-gram features, performing a forward-oriented dynamic routing between the capsules using the capsule parameters to extract global characteristics of the text corpus, and processing the text corpus according to the global characteristics using a long short-term memory layer to extract global sequential text dependencies from the text corpus, wherein parameters of the neural network are updated according to the local n-gram features, the capsule parameters, global characteristics, and global sequential text dependencies.
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