The document describes an unsupervised neural attention model for aspect extraction. It contains the following key points:
1. The model uses an attention mechanism in an autoencoder framework to extract aspect terms from sentences in an unsupervised manner.
2. The attention weights are used to obtain embeddings for aspect terms, which are then clustered to name the aspects.
3. The model achieves competitive performance compared to supervised models, demonstrating the effectiveness of the unsupervised attention mechanism for aspect extraction.
2. An Unsupervised Neural Attention Model for Aspect Extraction
ACL 2017
Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier
• Ruidan He
• https://aclanthology.coli.uni-saarland.de/papers/P17-1036/p17-1036
• Code https://github.com/ruidan/Unsupervised-Aspect-Extraction
2
6. aspect extraction
— Aspect extraction
Aspect : the target of opinion( ) entity
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• Aspect Extraction
1.
The beef was tender and melted in my mouth
The beef was tender and melted in my mouth
0.04 0.5 0.1 0.2 0.03 0.2 0.06 0 0.1
Model
8. 8
•
1.
aspect term food aspect
service aspect term
aspect term attention
aspect
This pie looks delicious but the service is bad
attention 0.04 0.2 0.1 0.2 0.03 0 0.2 0 0.1
attention 0.01 0.4 0.04 0.3 0.02 0 0.5 0 0.3
10. • Attention in CV
Attention:
scoring function attention weights
10
1.
11. 11
encoder layer word
hidden state decoder layer
attention model
hidden state
1.
• Attention in NLP
Image from https://blog.heuritech.com/2016/01/20/attention-mechanism/
Machine Translation: English to French
12. 12
1.
2.
• Attention Model
• Autoencoder Part(unsupervised)
• Aspect Term Embedding Extraction
• Aspect Naming
3.
4.
13. 13
•
1.
— aspect term
(e.g. “beef”, “pork”, “service”)
— aspect term
aspect
(e.g., cluster “beef ”, “pork”, “pasta” , and “tomato”into aspect “food” )
2.