Interventional Few-Shot Learning 星级: 23 页 Entailment as Few-Shot Learner 星级: 15 页 Defensive Few-shot Learning 星级: 10 页 Few-Shot Adversarial Domain Adaptation 星级: 11 页 Semi Few-Shot Attribute Translation 星级: 9 页 Large Margin Few-Shot Learning 星级: 17 页 Improved Few...
In such methods, recognition is understood as answering questions about named entities. The system must return valid named entities of the required type that are referenced in the sentence. In particular, we study the effect of prompts on the nested named entity recognition in few-shot setting. ...
任务是辨别Ourey Example中的实体。 为了解决这样的问题,很多方法遵循传统的序列标注策略,这样做的缺点是存在很多没意义的O实体,而且如果出现比较依赖token本身,面对nested NER之类的情况时效果会变得很差。由此又产生了一系列的二阶段方法,将NER任务解构成两阶段任务,分别是span extraction和mention classification。具体来...
In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address. This leads to the development of few-shot nested NER, where the prevalence of pretrained language models with in-context learning (ICL) offers promising solutions. ...
解读:Few-shot classification in Named Entity Recognition Task 1 介绍 2 相关工作 3 原型网络 3.1 模型 3.2 适配NER 4 小样本实体识别 4.1 形式化任务 4.2 基本模型 4.3 实验 5 实验设置 5.1 数据集 5.2 数据准备:模拟几次实验 5.3 实验设计 5.4 模型参数 6 结果 6.1 模型的性能 6.2 ... ...
Named Entity Recognition without Labelled Data: A Weak Supervision Approach Pyramid: A Layered Model for Nested Named Entity Recognition Sources of Transfer in Multilingual Named Entity Recognition Temporally-Informed Analysis of Named Entity Recognition ...
Recently, data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recognition (NER). However, conventional NER DA methods are mostly aimed at sequence labeling models, i.e., token-level cla...
实体关系抽取几篇论文 1、《A Unified MRC Framework for Named Entity Recognition》 paper:https://arxiv.org/pdf/1910.11476.pdf code:https://github.com/ShannonAI/mrc-for-flat-nested-ner 摘要:提出了一种基于MRC的框架提高了Flat NER和Nested NER的准确率......
(FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL. However, most prior works ...
In view of the fact that entity nested and professional terms are difficult to identify in the field of power dispatch, a multi-task-based few-shot named entity recognition model (FSPD-NER) for power dispatch is proposed. The model consists of four modules: featur...