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

[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