Papers by Minghua Nuo
Knowledge Graph Entity Typing with Curriculum Contrastive Learning (2025.coling-main)
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| Challenge: | Existing knowledge graphs suffer from incomplete type annotations because they are manually constructed by domain experts. |
| Approach: | They propose a CCLET model using the Curriculum Contrastive Learning strategy for KGET to fuse the entity related semantic and the structural information of the Knowledge Graph (KG) they define the difficulty of the course by controlling the level of added noise and aim to accurately learn with curriculum contrastive learning strategy from easy to difficult. |
| Outcome: | The proposed model outperforms state-of-the-art models and is highly accurate across multiple learning environments. |
Hybrid of Spans and Table-Filling for Aspect-Level Sentiment Triplet Extraction (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is an emerging task in sentiment analysis research. |
| Approach: | They propose a model which combines span with table-filling to extract triplets from words . they use syntactic and contextual features to generate word-pair tables and convert them to span tables . |
| Outcome: | The proposed model achieves competitive results on a dataset with a large dataset. |