Entity recognition based on multi-task learning and self-consistent verification
US11675978B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 6, 2021 |
| Grant date | Jun 13, 2023 |
| Priority date | — |
| Expiry date | Aug 8, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/044
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An approach is provided for improving a named entity recognition. Using a multi-label classification in a neural network, a sub-entity is identified in an original sentence. First and second labels are determined indicating first and second candidate types of the sub-entity. First and second replacement sentences are generated. The first replacement sentence replaces the sub-entity in the original sentence with a first sub-entity of the first candidate type. The second replacement sentence replaces the sub-entity in the original sentence with a second sub-entity of the second candidate type. First and second confidence scores for the first and second replacement sentences are determined. Based on the first confidence score exceeding the second confidence score by more than a threshold amount, the neural network is retrained by selecting the first instead of the second candidate type as the sub-entity type.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.