Papers by Ahmed Murtadha
DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis (2021.findings-acl)
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| 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. |
Naive Bayes-based Context Extension for Large Language Models (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have shown promising in-context learning abilities, but are often impeded by length limitations of transformer architecture. |
| Approach: | They propose a framework to enable existing Large Language Models to perform ICL with an increased number of demonstration examples by significantly expanding their context size. |
| Outcome: | The proposed framework outperforms existing methods in in-context learning with an increased number of demonstration examples while preserving linear efficiency. |
Rank-Aware Negative Training for Semi-Supervised Text Classification (2023.tacl-1)
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| Challenge: | Semi-supervised text classification-based paradigms employ the spirit of self-training, but the accuracy of pseudo-labels can be a problem in real-world scenarios. |
| Approach: | They propose a Rank-aware Negative Training framework to address SSTC in noisy label learning . they rank unlabeled texts based on evidential support from the labeled texts. |
| Outcome: | The proposed framework overcomes state-of-the-art alternatives and achieves competitive performance in other scenarios. |