Machine learning for joint recognition and assertion regression of elements in text
US11755838B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 14, 2020 |
| Grant date | Sep 12, 2023 |
| Priority date | — |
| Expiry date | Nov 11, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/126
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computing machine receives an input comprising unstructured text. The computing machine identifies, within the unstructured text, one or more entities using a named entity recognition (NER) engine in a trained machine learning model. The trained machine learning model embeds tokens from the text into a vector space and uses generated embeddings to identify one or more tokens as being associated with the one or more entities. The computing machine determines, using the trained machine learning model that identifies the one or more entities and based on the embedded tokens, an assertion applied, within the text, to at least one entity. The assertion is represented as a vector in a multi-dimensional space. Each dimension corresponds to a part of the assertion. The trained machine learning model is a span-level model that both identifies the one or more entities and determines the assertion based on candidate spans of tokens.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.