Invention Grant
- Patent Title: Unsupervised neural attention model for aspect extraction
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Application No.: US15484577Application Date: 2017-04-11
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Publication No.: US10755174B2Publication Date: 2020-08-25
- Inventor: Ruidan He , Daniel Dahlmeier
- Applicant: SAP SE
- Applicant Address: DE Walldorf
- Assignee: SAP SE
- Current Assignee: SAP SE
- Current Assignee Address: DE Walldorf
- Agency: Fish & Richardson P.C.
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G06F40/30 ; G06F40/216 ; G06F40/289

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.
Public/Granted literature
- US20180293499A1 UNSUPERVISED NEURAL ATTENTION MODEL FOR ASPECT EXTRACTION Public/Granted day:2018-10-11
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