Papers with entity span detection
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)
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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. |
| Approach: | They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning. |
| Outcome: | The proposed method improves on in-domain learning and domain adaptation in low-resource settings. |
ReCoT-NER: Enhancing Zero-Shot Named Entity Recognition through Chain-of-Thought Prompting and Recall-Oriented Loss Optimization (2026.findings-acl)
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| Challenge: | Named Entity Recognition (NER) is a key component of natural language processing (NLP) but it is difficult to implement in specialized domains such as wind power fault diagnosis. |
| Approach: | They propose a reasoning-enhanced generative framework that integrates Chain-of-Thought prompting and recall-oriented loss optimization to address these challenges. |
| Outcome: | The proposed framework improves recall and overall F1 performance across general and industrial domains. |
Direct Fact Retrieval from Knowledge Graphs without Entity Linking (2023.acl-long)
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| Challenge: | Existing methods to retrieve facts from Knowledge Graphs (KGs) require additional labels and may accumulate errors . |
| Approach: | They propose a framework that directly retrieves facts from KGs given input text based on their representational similarities. |
| Outcome: | The proposed framework outperforms baselines on multiple fact retrieval tasks. |
Named Entity Recognition Only from Word Embeddings (2020.emnlp-main)
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| Challenge: | Existing named entity recognition systems require large amounts of human annotated training data. |
| Approach: | They propose a fully unsupervised named entity recognition model which takes clues from pre-trained word embeddings. |
| Outcome: | The proposed model can be trained on two CoNLL benchmark datasets without annotating lexicon or corpus. |