Word embedding with disentangling prior
US11947908B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 7, 2021 |
| Grant date | Apr 2, 2024 |
| Priority date | — |
| Expiry date | Aug 30, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Described herein are system and method embodiments to improve word representation learning. Embodiments of a probabilistic prior may seamlessly integrate statistical disentanglement with word embedding. Different from previous deterministic methods, word embedding may be taken as a probabilistic generative model, and it enables imposing a prior that may identify independent factors generating word representation vectors. The probabilistic prior not only enhances the representation of word embedding, but also improves the model's robustness and stability. Furthermore, embodiments of the disclosed method may be flexibly plugged in various word embedding models. Extensive experimental results show that embodiments of the presented method may improve word representation on different tasks.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.