Method and system for text classification based on learning of transferable feature representations from a source domain
US10832166B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 20, 2016 |
| Grant date | Nov 10, 2020 |
| Priority date | — |
| Expiry date | Sep 11, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving real-time input data comprising labeled instances of the source domain and unlabeled instances of the target domain from a computing device. The method further includes determining source specific representation corresponding to the source domain and a common representation shared between the source domain and the target domain. Based on a positive contribution from the source specific representation and the common representation, the labeled instances of the source domain are classified. The method further includes training a generalized classifier based on a positive contribution from the common representation. The method further includes automatically performing text classification on the unlabeled instances of the target domain based on the trained generalized classifier. The result of the text classification is rendered at a user interface of the computing device.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.