Real time learning of text classification models for fast and efficient labeling of training data and customization
US10896385B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 27, 2017 |
| Grant date | Jan 19, 2021 |
| Priority date | — |
| Expiry date | Sep 11, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/35
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for real-time generation and customization of text classification models. An initial dataset of input text samples are manually assigned labels, and the labeled input text samples are tokenized and provided as training data to train machine learning classifiers for various classes or categories of the input text samples. As the machine learning classifiers train with the training data, feedback in the form of suggestions (or predictions) are provided in real time by the text classification models regarding which label(s) to assign to any input text sample(s) currently in the training data or any new input text sample(s) further provided as training data for the respective machine learning classifiers. The suggested (or predicted) label(s) can be manually assigned to the input text sample(s), if deemed appropriate, and the newly labeled input text sample(s) can be provided to supplement the existing training data for the respective machine learning classifiers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.