On-demand relation extraction from text
US11151175B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 24, 2018 |
| Grant date | Oct 19, 2021 |
| Priority date | — |
| Expiry date | Dec 24, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/041
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
One embodiment provides a method for on-demand relation extraction from unstructured text that includes obtaining a text corpus of domain related unstructured text. Representations of the unstructured text that capture entity-specific syntactic knowledge are created. Initial user seeds of informative examples containing relations are received. Extraction models in a neural network are trained using the initial user seeds. Performance information and a confidence score are provided for each prediction for each extraction model. A next batch of informative examples are identified for annotation from the text corpus based on training a neural network classifier on a pool of labeled informative examples. Stopping criteria is determined based on differences of the performance information and the confidence score in relation to parameters for each extraction model. Based on the stopping criteria, it is determined whether to retrain a particular extraction model after the informative examples have been labeled.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.