NAACL2022信息抽取论文分类
目录
1、Named Entity Recognition
2、Relation Extraction
3、Event Extraction
4、Universal Information Extraction
1、Named Entity Recognition
[1] Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting
[2] ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition
[3] Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition
[4] Sentence-Level Resampling for Named Entity Recognition
[5] Hero-Gang Neural Model For Named Entity Recognition
[7] On the Use of External Data for Spoken Named Entity Recognition
[8] Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition
[9] Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition
[10] MultiNER: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition
[11] NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension
2、Relation Extraction
2.1 Document RE
[12] Few-Shot Document-Level Relation Extraction
[13] Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
[14] Modeling Explicit Task Interactions in Document-Level Joint Entity and Relation Extraction
[15] Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction
[16] SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction
2.2 Few-shot or Zero-shot
[12] Few-Shot Document-Level Relation Extraction
[17] RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction
[18] HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
[19] Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction
2.3 Constrative Learning
[18] HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
[17] RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction
2.4 Distant Supervision
[19] Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision
2.5 Others
[20] Modeling Multi-Granularity Hierarchical Features for Relation Extraction
[21] A Dataset for N-ary Relation Extraction of Drug Combinations
[22] Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
[23] Generic and Trend-aware Curricula for Relation Extraction in Text Graphs
[24] Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration
[25] GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction
[26] Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
[27] Dependency Position Encoding for Relation Extraction
3、Event Extraction
[29] Cross-Lingual Event Detection via Optimized Adversarial Training
[30] A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
[31] RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction
[32] DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction
[33] Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities
[34] Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss
[35] MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection
[36] Event Schema Induction with Double Graph Autoencoders
[37] DEGREE: A Data-Efficient Generation-Based Event Extraction Model
[38] Go Back in Time: Generating Flashbacks in Stories with Event Plots and Temporal Prompts
[39] Improving Consistency with Event Awareness for Document-Level Argument Extraction
[40] Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction
[41] Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
[42] Event Detection for Suicide Understanding
[43] Extracting Temporal Event Relation with Syntax-guided Graph Transformer
4、Universal Information Extraction
[44] Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies
NAACL2022信息抽取论文分类相关推荐
- EMNLP 2021信息抽取论文合集
EMNLP是由国际计算语言学协会下属特殊兴趣小组SIGDAT发起并组织的系列会议,是自然语言处理领域顶级的国际学术会议之一.EMNLP 2021 将于 11 月 7 日 - 11 日进行,一共接收了6 ...
- ACL2021 | 信息抽取相关论文
今日ACL2021放出长文接受列表了(可点击[阅读原文]查阅),JayJay对信息抽取论文做了分类汇总,希望对大家有所帮助- 一.实体抽取 " 实体抽取主要涉及嵌套NER.非连续NER.中文 ...
- 信息抽取(四)【NLP论文复现】Multi-head Selection和Deep Biaffine Attention在关系抽取中的实现和效果
Multi-head Selection和Deep Biaffine Attention在关系抽取中的应用 前言 Multi-head Selection 一.Joint entity recogni ...
- NLP专栏简介:数据增强、智能标注、意图识别算法|多分类算法、文本信息抽取、多模态信息抽取、可解释性分析、性能调优、模型压缩算法等
NLP专栏简介:数据增强.智能标注.意图识别算法|多分类算法.文本信息抽取.多模态信息抽取.可解释性分析.性能调优.模型压缩算法等 专栏链接:NLP领域知识+项目+码源+方案设计 订阅本专栏你能获得什 ...
- 深度学习应用篇-自然语言处理[10]:N-Gram、SimCSE介绍,更多技术:数据增强、智能标注、多分类算法、文本信息抽取、多模态信息抽取、模型压缩算法等
[深度学习入门到进阶]必看系列,含激活函数.优化策略.损失函数.模型调优.归一化算法.卷积模型.序列模型.预训练模型.对抗神经网络等 专栏详细介绍:[深度学习入门到进阶]必看系列,含激活函数.优化策略 ...
- [论文阅读笔记70]基于token-token grid模型的信息抽取(5篇)
论文1: TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking 年份 ...
- 论文浅尝 | GraphIE:基于图的信息抽取框架
笔记整理:吕欣泽,南京大学计算机科学与技术系,硕士研究生. 论文连接:https://arxiv.org/pdf/1810.13083.pdf 发表会议:NAACL 2019 摘要 大多数现代信息提取 ...
- C.8 基于ERNIELayoutPDFplumber-UIEX的多方案学术论文信息抽取
NLP专栏简介:数据增强.智能标注.意图识别算法|多分类算法.文本信息抽取.多模态信息抽取.可解释性分析.性能调优.模型压缩算法等 专栏详细介绍:NLP专栏简介:数据增强.智能标注.意图识别算法|多分 ...
- 科研常识:论文分类、收录情况以及相关信息查询方式等
1 论文分类 论文根据不同的特性可以进行相应的分类,比较常见的是根据出版的途径进行分类.按照出版的途径,论文可以分为:学位论文.期刊论文.会议论文. 学位论文包括学士学位论文(batchelor th ...
最新文章
- 全球最大资管公司押注人工智能!要做这些大事
- javascript实现缩略图
- 新版pycharm,亮瞎我的狗眼
- linux软中断分析,linux操作系统下的软中断问题分析_linux教程
- B站爱情怀,投资者只看利益
- 利用Contained Database和DAC来开发基于SQL Server Denali和SQL Azure之上的应用程序
- 正则表达式及其在python上的应用
- 游戏提高性能 游戏降帧处理
- SpringCloud Ribbon之概述(一)
- 为什么回归直线过平均值点_线性回归和梯度下降的初学者教程
- C++基础:C++的封装/继承/多态
- Linux系统编程——vfork() 函数详解
- Windows下串口驱动安装
- 逃离北上广:你以为回到小城市就非常幸福了吗?
- K倍交叉验证配对t检验
- 一分钟详解智能快递柜锁控板方案和原理
- Excel VBA 编程的常用代码
- Android Studio将html5网址封装成APP
- 我在Blue Nile(蓝色尼罗河)上通过python爬取一百万颗钻石,最终选出心仪的一颗
- kotlin Anko的实际用法