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
[6]Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation
[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
[12]HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
[13] Few-Shot Document-Level Relation Extraction
[14]Modeling Multi-Granularity Hierarchical Features for Relation Extraction
[15]A Dataset for N-ary Relation Extraction of Drug Combinations
[16]Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
[17]Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
[18]SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction
[19]Generic and Trend-aware Curricula for Relation Extraction in Text Graphs
[20]Modeling Explicit Task Interactions in Document-Level Joint Entity and Relation Extraction
[21]Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction
[22]RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction
[23]Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration
[24]Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction
[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
[28]Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision
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