Selectively generating word vector and paragraph vector representations of fields for machine learning
US10459962B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 19, 2018 |
| Grant date | Oct 29, 2019 |
| Priority date | — |
| Expiry date | Sep 19, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.