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        <title>Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Yongqi</namePart>
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    <name type="personal">
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            <namePart type="given">Houda</namePart>
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            <namePart type="given">Juan</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the over-detected false spans at span detection stage and the inaccurate and unstable prototypes at type classification stage remain to be challenging problems. In this paper, we propose a novel Type-Aware Decomposed framework, namely TadNER, to solve these problems. We first present a type-aware span filtering strategy to filter out false spans by removing those semantically far away from type names. We then present a type-aware contrastive learning strategy to construct more accurate and stable prototypes by jointly exploiting support samples and type names as references. Extensive experiments on various benchmarks prove that our proposed TadNER framework yields a new state-of-the-art performance.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.598</identifier>
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        <url>https://aclanthology.org/2023.findings-emnlp.598</url>
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        <date>2023-12</date>
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            <start>8911</start>
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%0 Conference Proceedings
%T Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
%A Li, Yongqi
%A Yu, Yu
%A Qian, Tieyun
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-type-aware
%X Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the over-detected false spans at span detection stage and the inaccurate and unstable prototypes at type classification stage remain to be challenging problems. In this paper, we propose a novel Type-Aware Decomposed framework, namely TadNER, to solve these problems. We first present a type-aware span filtering strategy to filter out false spans by removing those semantically far away from type names. We then present a type-aware contrastive learning strategy to construct more accurate and stable prototypes by jointly exploiting support samples and type names as references. Extensive experiments on various benchmarks prove that our proposed TadNER framework yields a new state-of-the-art performance.
%R 10.18653/v1/2023.findings-emnlp.598
%U https://aclanthology.org/2023.findings-emnlp.598
%U https://doi.org/10.18653/v1/2023.findings-emnlp.598
%P 8911-8927

Markdown (Informal)

[Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition](https://aclanthology.org/2023.findings-emnlp.598) (Li et al., Findings 2023)

• Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition (Li et al., Findings 2023)

ACL

• Yongqi Li, Yu Yu, and Tieyun Qian. 2023. Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8911–8927, Singapore. Association for Computational Linguistics.