@inproceedings{zhang-etal-2024-kcl-shot,
title = "{KCL}: Few-shot Named Entity Recognition with Knowledge Graph and Contrastive Learning",
author = "Zhang, Shan and
Cao, Bin and
Fan, Jing",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.846",
pages = "9681--9692",
abstract = "Named Entity Recognition(NER), as a crucial subtask in natural language processing(NLP), is limited to a few labeled samples(a.k.a. few-shot). Metric-based meta-learning methods aim to learn the semantic space and assign the entity to its nearest label based on the similarity of their representations. However, these methods have trouble with semantic space learning and result in suboptimal performance. Specifically, the label name or its description is widely used for label semantic representation learning, but the label information extracted from the existing label description is limited. In addition, these methods focus on reducing the distance between the entity and the corresponding label, which may also reduce the distance between the labels and thus cause misclassification. In this paper, we propose a few-shot NER method that harnesses the power of Knowledge Graph and Contrastive Learning to improve the prototypical semantic space learning. First, KCL leverages knowledge graphs to provide rich and structured label information for label semantic representation learning. Then, KCL introduces the idea of contrastive learning to learn the label semantic representation. The label semantic representation is used to help distance the label clusters in the prototypical semantic space to reduce misclassification. Extensive experiments show that KCL achieves significant improvement over the state-of-the-art methods.",
}
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<abstract>Named Entity Recognition(NER), as a crucial subtask in natural language processing(NLP), is limited to a few labeled samples(a.k.a. few-shot). Metric-based meta-learning methods aim to learn the semantic space and assign the entity to its nearest label based on the similarity of their representations. However, these methods have trouble with semantic space learning and result in suboptimal performance. Specifically, the label name or its description is widely used for label semantic representation learning, but the label information extracted from the existing label description is limited. In addition, these methods focus on reducing the distance between the entity and the corresponding label, which may also reduce the distance between the labels and thus cause misclassification. In this paper, we propose a few-shot NER method that harnesses the power of Knowledge Graph and Contrastive Learning to improve the prototypical semantic space learning. First, KCL leverages knowledge graphs to provide rich and structured label information for label semantic representation learning. Then, KCL introduces the idea of contrastive learning to learn the label semantic representation. The label semantic representation is used to help distance the label clusters in the prototypical semantic space to reduce misclassification. Extensive experiments show that KCL achieves significant improvement over the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T KCL: Few-shot Named Entity Recognition with Knowledge Graph and Contrastive Learning
%A Zhang, Shan
%A Cao, Bin
%A Fan, Jing
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zhang-etal-2024-kcl-shot
%X Named Entity Recognition(NER), as a crucial subtask in natural language processing(NLP), is limited to a few labeled samples(a.k.a. few-shot). Metric-based meta-learning methods aim to learn the semantic space and assign the entity to its nearest label based on the similarity of their representations. However, these methods have trouble with semantic space learning and result in suboptimal performance. Specifically, the label name or its description is widely used for label semantic representation learning, but the label information extracted from the existing label description is limited. In addition, these methods focus on reducing the distance between the entity and the corresponding label, which may also reduce the distance between the labels and thus cause misclassification. In this paper, we propose a few-shot NER method that harnesses the power of Knowledge Graph and Contrastive Learning to improve the prototypical semantic space learning. First, KCL leverages knowledge graphs to provide rich and structured label information for label semantic representation learning. Then, KCL introduces the idea of contrastive learning to learn the label semantic representation. The label semantic representation is used to help distance the label clusters in the prototypical semantic space to reduce misclassification. Extensive experiments show that KCL achieves significant improvement over the state-of-the-art methods.
%U https://aclanthology.org/2024.lrec-main.846
%P 9681-9692
Markdown (Informal)
[KCL: Few-shot Named Entity Recognition with Knowledge Graph and Contrastive Learning](https://aclanthology.org/2024.lrec-main.846) (Zhang et al., LREC-COLING 2024)
ACL