System and method for learning latent representations for natural language tasks
US9720907B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 14, 2015 |
| Grant date | Aug 1, 2017 |
| Priority date | — |
| Expiry date | Sep 14, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F40/40
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for learning latent representations for natural language tasks. A system configured to practice the method analyzes, for a first natural language processing task, a first natural language corpus to generate a latent representation for words in the first corpus. Then the system analyzes, for a second natural language processing task, a second natural language corpus having a target word, and predicts a label for the target word based on the latent representation. In one variation, the target word is one or more word such as a rare word and/or a word not encountered in the first natural language corpus. The system can optionally assigning the label to the target word. The system can operate according to a connectionist model that includes a learnable linear mapping that maps each word in the first corpus to a low dimensional latent space.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.