Name
Title | ||
|---|---|---|
Bilateral-brain-like Semantic and Syntactic Cognitive Network for Aspect-level Sentiment Analysis |
Abstract | ||
|---|---|---|
Aspect-level sentiment analysis (ALSA) is a fine-grained task for classifying the sentiment polarity of a specific aspect in a sentence. In spite of the progress in deep-learning algorithms within natural language processing domain, the methods in line with human cognition are absent. Inspired by the processing principle of our brain, we propose a Bilateral-brainlike Semantic and Syntactic Cognitive Network (BSSCN) for aspect-level sentiment analysis. There are four major modules established in BSSCN, which are left hemisphere semantic activation (LH-SA), semantic selection (SS) module, semantic integration (SI) module and right hemisphere semantic activation (RH-SA). Experimental results on a variety of datasets show that our model stably outperforms the widely-used methods, which establishes a strong evidence of the effectiveness in ALSA tasks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534052 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
Bilateral-brain-like, Cognitive, Aspect-level Sentiment Analysis, GCN | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinpeng Chen | 1 | 0 | 0.34 |
| Zuohua Huang | 2 | 0 | 0.34 |
| Yun Xue | 3 | 11 | 3.59 |