Multi-task conditional random field models for sequence labeling
US9785891B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 9, 2014 |
| Grant date | Oct 10, 2017 |
| Priority date | — |
| Expiry date | Dec 2, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/016
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of a computer-implemented method for automatically analyzing a conversational sequence between multiple users are disclosed. The method includes receiving signals corresponding to a training dataset including multiple conversational sequences; extracting a feature from the training dataset based on predefined feature categories; formulating multiple tasks for being learned from the training dataset based on the extracted feature, each task related to a predefined label; and providing a model for each formulated task, the model including a set of parameters common to the tasks. The set includes an explicit parameter, which is explicitly shared with each of the formulated tasks. The method further includes optimizing a value of the explicit parameter to create an optimized model; creating a trained model for the formulated tasks using the optimized value of the explicit parameter; and assigning predefined labels for the formulated tasks to a live dataset based on the corresponding trained model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.