Predicting the temporal stability of answers in a deep question answering system
US10824659B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 28, 2018 |
| Grant date | Nov 3, 2020 |
| Priority date | — |
| Expiry date | Jan 22, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The temporal stability of an answer from a deep question answering system is predicted using a natural language classifier. A training corpus is divided into time-ordered slices having uniform granularity. A series of candidate answers to a training question is generated based on the slices, and a temporal profile for the series is identified by associating candidate answers with respective temporal intervals. The temporal profile is translated to a temporal stability value (representing a time period) using a temporal stability model. The classifier is trained using such training questions correlated with respective temporal stability values. Thereafter, when a user submits a natural language query to the deep question answering system, the query is also applied to the classifier which determines its temporal stability. The temporal stability is presented to the user with the answer to give a sense of how long the answer can be deemed reliable.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.