Invention Grant
- Patent Title: Text-to-vectorized representation transformation
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Application No.: US16934220Application Date: 2020-07-21
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Publication No.: US11663402B2Publication Date: 2023-05-30
- Inventor: Chao-Min Chang , Kuei-Ching Lee , Ci-Hao Wu , Chia-Heng Lin
- 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
- Agent Monchai Chuaychoo
- Main IPC: G06F40/242
- IPC: G06F40/242 ; G06F40/295 ; G06F40/58

Abstract:
An approach for a fast and accurate word embedding model, “desc2vec,” for out-of-dictionary (OOD) words with a model learning from the dictionary descriptions of the word is disclosed. The approach includes determining that a target text element is not in a set of reference text elements, information describing the target text element is obtained. The information comprises a set of descriptive text elements. A set of vectorized representations for the set of descriptive text elements is determined. A target vectorized representation for the target text element is determined based on the set of vectorized representations using a machine learning model. The machine learning model is trained to represent a predetermined association between the set of vectorized representations for the set of descriptive text elements describing the target text element and the target vectorized representation.
Public/Granted literature
- US20220027557A1 TEXT-TO-VECTORIZED REPRESENTATION TRANSFORMATION Public/Granted day:2022-01-27
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