Few-shot Named Entity Recognition with Self-describing Networks 用自描述网络进行少样本命名实体识别 acl 2022 原文地址:2022.acl-long.392.pdf (aclanthology.org) Abstract 少样本NER需要在有限制的实例中高效捕获信息,并且从外部资源中迁移有用的知识。在本文中,作者提出了一种用于少样本NER的自描述机制,它可以...
knowledge mismatch challenge:Few-shot NER需用从额外的资源中利用和迁移有用的知识,但是这些知识并不直接与新任务匹配,因为可能包含了不相关、参差甚至冲突的知识,即knowledge mismatch challenge。例如,”America”在Wikipedia,OntoNotes,WNUT17数据集中分别被分类为geographic entity,GPE,location。 本文的核心思想为:所有...
在本节中,我们正式确定了few-shot NER的任务,并提出了一个标准的评估设置,以便于对未来研究的结果进行有意义的比较 3.1 few-shot ner few shot-NER系统需要学习仅使用几个标记的示例来泛化到看不见的实体类 k-shot ner定义如下,输入序列x,k-shot支持集 for 目标tag集,找出序列x的标签y k-shot支持集:对于每...
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A first named entity recognition (NER) system may be adapted to create a second NER system that is able to recognize a new named entity using few-shot learning. The second NER system may process support tokens that provide one or more examples of the new named entity and may process input...
FewShot NER对于低资源域中的实体标记至关重要。现有的方法仅从源域学习特定于类的语义特征和中间表示。这会影响对看不见的目标域的通用性,从而导致性能不佳。为此,我们提出了CONTAINER,这是一种新的对比学习技术,它优化了标记间的分布距离。CONTAINER没有优化特定于类的属性,而是 优化了一个广义目标 ,即基于高斯...
Few-shot Named Entity Recognition with Self-describing Networks Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, Le Sun 2022 Multi-Granularity Dual-Aware Contrastive Learning for Few-shot Named Entity Recognition Boxiang Ma, Changzheng Wang, S...
Few-shot named entity recognition aims to extract entities by using a limited amount of annotated data. Recently, contrastive learning shows promising performance in few-shot named entity recognition. Despite the effectiveness of this method, there are still a few obstacles for few-shot named entity...
1. 介绍 FewShot NER对于低资源域中的实体标记至关重要。现有的方法仅从源域学习特定于类的语义特征和中间表示。这会影响对看不见的目标域的通用性,从而导致性能不佳。为...
2. Entity Recognition via Entity Generation 在SDNet 中,实体识别是通过实体生成来执行的,给定的实体生成提示 P_EG 和句子 X。具体来说,P_EG 以任务描述符 [EG] 开头,描述符后面是目标类型列表及其对应说明,即:P_{EG}=\{[EG]t_1\ :\{l_1^1,\dots,l_1^{m_1}\};t_2\ :\{l_2^1,\dots,...