Papers with GML
GAINER: Graph Machine Learning with Node-specific Radius for Classification of Short Texts and Documents (2024.eacl-long)
Copied to clipboard
| Challenge: | Recent advances in Graph Machine Learning (GML) have led to the development of numerous models tailored for processing text for various natural language applications. |
| Approach: | They propose a framework called Graph mAchine learnIng with Node-spEcific Radius that is aimed at graph-based NLP. |
| Outcome: | The proposed framework is non-neural and novel for graph-based NLP. |
Supervised Gradual Machine Learning for Aspect-Term Sentiment Analysis (2023.tacl-1)
Copied to clipboard
| Challenge: | Recent work shows that Aspect-Term Sentiment Analysis (ATSA) can be performed by Gradual Machine Learning (GML) but the current unsupervised solution is limited by inaccurate knowledge conveyance. |
| Approach: | They propose a supervised approach which leverages binary polarity relations between instances to enable supervised knowledge conveyance. |
| Outcome: | The proposed approach outperforms pure DNN solutions on real benchmark data. |
DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for Aspect-Term Sentiment Analysis (ATSA) use pre-specified lexicons to extract sentiment features. |
| Approach: | They propose a Deep Neural Network-driven approach for Aspect-Term Sentiment Analysis (ATSA) that leverages shared features between labeled and unlabeled instances for knowledge conveyance. |
| Outcome: | The proposed approach consistently achieves state-of-the-art performance on real benchmark data. |