Semantic parsing using deep neural networks for predicting canonical forms
US9858263B2 · kind B2 · utility
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Inventors
Key dates
| Filing date | May 5, 2016 |
| Grant date | Jan 2, 2018 |
| Priority date | — |
| Expiry date | May 19, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/16
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for predicting a canonical form for an input text sequence includes predicting the canonical form with a neural network model. The model includes an encoder, which generates a first representation of the input text sequence based on a representation of n-grams in the text sequence and a second representation of the input text sequence generated by a first neural network. The model also includes a decoder which sequentially predicts terms of the canonical form based on the first and second representations and a predicted prefix of the canonical form. The canonical form can be used, for example, to query a knowledge base or to generate a next utterance in a discourse.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.