Invention Grant
- Patent Title: Familiarity-based text classification framework selection
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Application No.: US15840559Application Date: 2017-12-13
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Publication No.: US11222058B2Publication Date: 2022-01-11
- Inventor: Ethan A. Geyer , Jonathan F. Brunn , Jonathan Dunne , Naama Tepper
- 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: Fabian VanCott
- Agent Steven L. Nichols
- Main IPC: G06F16/00
- IPC: G06F16/00 ; G06F16/35 ; H04L12/58 ; G06N3/08 ; G06F16/31

Abstract:
Familiarity-based text classification framework selection is described. A list of participants in an electronic message thread is selected. For each pairing of participants, a familiarity score is determined based on a number of criteria. A familiarity model is formed based on multiple familiarity scores and a text classification framework for the electronic message thread is selected based on the familiarity model.
Public/Granted literature
- US20190179955A1 FAMILIARITY-BASED TEXT CLASSIFICATION FRAMEWORK SELECTION Public/Granted day:2019-06-13
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