Unsupervised neural attention model for aspect extraction
US10755174B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 11, 2017 |
| Grant date | Aug 25, 2020 |
| Priority date | — |
| Expiry date | Jun 3, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0895
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and computer-readable storage media for receiving a vocabulary, the vocabulary including text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file, associating each word in the vocabulary with a feature vector, providing a sentence embedding for each sentence of the vocabulary based on a plurality of feature vectors to provide a plurality of sentence embeddings, providing a reconstructed sentence embedding for each sentence embedding based on a weighted parameter matrix to provide a plurality of reconstructed sentence embeddings, and training the unsupervised neural attention model based on the sentence embeddings and the reconstructed sentence embeddings to provide a trained neural attention model, the trained neural attention model being used to automatically determine aspects from the vocabulary.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.