Papers by Arzoo Katiyar
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning (2022.acl-long)
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| Challenge: | Existing methods for Named Entity Recognition only learn class-specific semantic features and intermediate representations from source domains, resulting in suboptimal performance. |
| Approach: | They propose a contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. |
| Outcome: | The proposed technique outperforms existing methods by 3%-13% absolute F1 points while showing consistent performance trends. |
Nested Named Entity Recognition Revisited (N18-1)
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| Challenge: | Existing methods for named entity recognition and mention detection ignore nested entities along with any semantic relations between them. |
| Approach: | They propose a recurrent neural network-based approach to handle named entity recognition and nested entity mention detection simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three standard datasets. |
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning (2020.emnlp-main)
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| Challenge: | Named entity recognition (NER) is widely adopted in several domains, such as news, medical, and social media. |
| Approach: | They propose a few-shot named entity recognition system based on nearest neighbor learning and structured inference. |
| Outcome: | The proposed method improves F1 scores on standard few-shot NER evaluation tasks by 6% to 16% relative to previous methods. |