└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Information-Extraction-Papers 2 | 信息抽取相关顶会论文。 3 | 4 | # 信息抽取 5 | ## AAAI 2023 6 | - Universal Information Extraction as Unified Semantic Matching 7 | [PDF](https://arxiv.org/pdf/2301.03282.pdf) 8 | 9 | ## SIGIR 2023 10 | - Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction 11 | [PDF](https://arxiv.org/abs/2210.10709) 12 | [CODE](https://github.com/zjunlp/RAP) 13 | 14 | ## ACL 2022 15 | - Text-to-Table: A New Way of Information Extraction 16 | [PDF](https://arxiv.org/pdf/2109.02707) 17 | [CODE](https://github.com/shirley-wu/text_to_table) 18 | - Unified Structure Generation for Universal Information Extraction 19 | [PDF](https://arxiv.org/pdf/2203.12277) 20 | [CODE](https://github.com/universal-ie/UIE) 21 | - FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction 22 | [PDF](https://arxiv.org/pdf/2203.08411) 23 | - Automatic Error Analysis for Document-level Information Extraction 24 | [PDF](https://aclanthology.org/2022.acl-long.274/) 25 | [CODE](https://github.com/IceJinx33/auto-err-template-fill/) 26 | - BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation 27 | [PDF](https://aclanthology.org/2022.acl-long.307/) 28 | [CODE](https://github.com/gkiril/benchie) 29 | - OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework 30 | [PDF](https://aclanthology.org/2022.acl-long.430/) 31 | - MILIE: Modular & Iterative Multilingual Open Information Extraction 32 | [PDF](https://aclanthology.org/2022.acl-long.478/) 33 | 34 | ## EMNLP 2022 35 | - HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response 36 | - Unsupervised Domain Adaptation for Joint Information Extraction 37 | - Syntactically Robust Training on Partially-Observed Data for Open Information Extraction 38 | - Syntactic Multi-view Learning for Open Information Extraction 39 | [PDF](https://arxiv.org/pdf/2212.02068) 40 | [CODE](https://github.com/daviddongkc/smile_oie) 41 | - A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction 42 | - Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction 43 | - IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models 44 | - Towards Generalized Open Information Extraction 45 | 46 | ## COLING 2022 47 | - Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts 48 | 49 | ## NAACL 2022 50 | - GenIE: Generative Information Extraction 51 | [PDF](https://arxiv.org/pdf/2112.08340) 52 | [CODE](https://github.com/epfl-dlab/GenIE) 53 | - CompactIE: Compact Facts in Open Information Extraction 54 | [PDF](https://arxiv.org/pdf/2205.02880) 55 | [CODE](https://github.com/FarimaFatahi/CompactIE) 56 | - GMN: Generative Multi-modal Network for Practical Document Information Extraction 57 | [PDF](https://arxiv.org/pdf/2207.04713) 58 | - Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies 59 | [PDF](https://aclanthology.org/2022.naacl-main.324.pdf) 60 | 61 | # 命名实体识别 62 | ## AAAI 2023 63 | - ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition 64 | [PDF](https://arxiv.org/pdf/2301.08855) 65 | - Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble 66 | [PDF](https://arxiv.org/pdf/2212.06522) 67 | [CODE](https://github.com/zenhjunpro/ATSEN) 68 | - Nested Named Entity Recognition as Building Local Hypergraphs 69 | - A Neural Span-Based Continual Named Entity Recognition Model 70 | - AUC Maximization for Low-Resource Named Entity Recognition 71 | [PDF](https://arxiv.org/pdf/2212.04800) 72 | - MNER-QG: An End-to-End MRC Framework for Multimodal Named Entity Recognition with Query Grounding 73 | [PDF](https://arxiv.org/pdf/2211.14739) 74 | 75 | ## ACL 2022 76 | - KinyaBERT: a Morphology-aware Kinyarwanda Language Model 77 | [PDF](https://arxiv.org/abs/2203.08459) 78 | [CODE](https://github.com/anzeyimana/kinyabert-acl2022) 79 | - Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks 80 | [PDF](https://arxiv.org/pdf/2110.05419) 81 | [CODE](https://github.com/sustcsonglin/pointer-net-for-nested) 82 | - Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition 83 | [PDF](https://arxiv.org/pdf/2203.10693) 84 | [CODE](https://github.com/GT-SALT/Guided-Adversarial-Augmentation) 85 | - Decomposed Meta-Learning for Few-Shot Named Entity Recognition 86 | [PDF](https://arxiv.org/pdf/2204.05751) 87 | [CODE](https://github.com/microsoft/vert-papers/tree/master/papers/DecomposedMetaNER) 88 | - Thai Nested Named Entity Recognition Corpus 89 | [PDF](https://aclanthology.org/2022.findings-acl.116/) 90 | [CODE](https://github.com/vistec-AI/Thai-NNER) 91 | - An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition 92 | [PDF](https://arxiv.org/pdf/2109.07589) 93 | - Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning 94 | [PDF](https://aclanthology.org/2022.acl-long.14/) 95 | - Boundary Smoothing for Named Entity Recognition 96 | [PDF](https://arxiv.org/pdf/2204.12031) 97 | [CODE](https://github.com/syuoni/eznlp) 98 | - CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning 99 | [PDF](https://arxiv.org/pdf/2204.09589) 100 | [CODE](https://github.com/psunlpgroup/CONTaiNER) 101 | - Few-Shot Class-Incremental Learning for Named Entity Recognition 102 | [PDF](https://aclanthology.org/2022.acl-long.43/) 103 | - Few-shot Named Entity Recognition with Self-describing Networks 104 | [PDF](https://arxiv.org/pdf/2203.12252) 105 | - FiNER: Financial Numeric Entity Recognition for XBRL Tagging 106 | [PDF](https://arxiv.org/pdf/2203.06482) 107 | [CODE](https://github.com/nlpaueb/finer) 108 | - MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER 109 | [PDF](https://aclanthology.org/2022.acl-long.160/) 110 | [CODE](https://github.com/RandyZhouRan/MELM/) 111 | - MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic 112 | [PDF](https://arxiv.org/pdf/2204.04391) 113 | [CODE](https://github.com/BeyonderXX/MINER) 114 | - Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing 115 | [PDF](https://arxiv.org/pdf/2203.04665) 116 | [CODE](https://github.com/LouChao98/nner_as_parsing) 117 | - Nested Named Entity Recognition with Span-level Graphs 118 | [PDF](https://aclanthology.org/2022.acl-long.63/) 119 | - Parallel Instance Query Network for Named Entity Recognition 120 | [PDF](https://arxiv.org/pdf/2203.10545) 121 | [CODE](https://github.com/tricktreat/piqn) 122 | - Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training 123 | [PDF](https://aclanthology.org/2022.findings-acl.9/) 124 | - Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples 125 | [PDF](https://aclanthology.org/2022.findings-acl.179/) 126 | - Cross-domain Named Entity Recognition via Graph Matching 127 | [PDF](https://aclanthology.org/2022.findings-acl.210/) 128 | - Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking 129 | [PDF](https://aclanthology.org/2022.findings-acl.225/) 130 | [CODE](https://github.com/tudou0002/NEAT) 131 | - Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition 132 | [PDF](https://arxiv.org/pdf/2110.07480) 133 | [CODE](https://github.com/GanjinZero/Triaffine-nested-ner) 134 | - Label Semantics for Few Shot Named Entity Recognition 135 | [PDF](https://arxiv.org/pdf/2203.08985) 136 | - Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework 137 | [PDF](https://arxiv.org/pdf/2204.05819) 138 | [CODE](https://github.com/zlwang-cs/LASER-release) 139 | 140 | ## EMNLP 2022 141 | - Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition 142 | - DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition 143 | - Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition 144 | - Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition 145 | - Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition 146 | - SetGNER: General Named Entity Recognition as Entity Set Generation 147 | - SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition 148 | - Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition 149 | - MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition 150 | - Wider & Closer: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition 151 | - Simple Questions Generate Named Entity Recognition Datasets 152 | - A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition 153 | - Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition 154 | - ConNER: Consistency Training for Cross-lingual Named Entity Recognition 155 | - SLICER: Sliced Fine-Tuning for Low-Resource Cross-Lingual Transfer for Named Entity Recognition 156 | - CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation 157 | - TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition 158 | 159 | ## NAACL 2022 160 | - Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting 161 | [PDF](http://114.215.220.151:8000/20220503/Robust%20Self-Augmentation%20for%20Named%20Entity%20Recognition%20with%20Meta%20Reweighting.pdf) 162 | [CODE](https://github.com/LindgeW/MetaAug4NER) 163 | - ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition 164 | [PDF](https://arxiv.org/pdf/2112.06482) 165 | [CODE](https://github.com/Alibaba-NLP/KB-NER/tree/main/ITA) 166 | - Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition 167 | [PDF](https://aclanthology.org/2022.findings-naacl.203.pdf) 168 | [CODE](https://github.com/yinghy18/CReDEL) 169 | - Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition 170 | [PDF](https://aclanthology.org/2022.naacl-main.200.pdf) 171 | - Sentence-Level Resampling for Named Entity Recognition 172 | [PDF](https://aclanthology.org/2022.naacl-main.156.pdf) 173 | [CODE](https://github.com/XiaoChen-W/NER_Adaptive_Resampling) 174 | - On the Use of External Data for Spoken Named Entity Recognition 175 | [PDF](https://arxiv.org/pdf/2112.07648) 176 | [CODE](https://github.com/asappresearch/spoken-ner) 177 | - Hero-Gang Neural Model For Named Entity Recognition 178 | [PDF](https://arxiv.org/pdf/2205.07177) 179 | [CODE](https://github.com/jinpeng01/HGN) 180 | - Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition 181 | [PDF](https://arxiv.org/pdf/2204.05544) 182 | - MultiNER: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition 183 | [PDF](https://aclanthology.org/2022.findings-naacl.60.pdf) 184 | [CODE](https://github.com/Babelscape/multinerd) 185 | 186 | ## COLING 2022 187 | - LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting 188 | [PDF](https://arxiv.org/abs/2109.00720) 189 | [CODE](https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot) 190 | - COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition 191 | [PDF](https://aclanthology.org/2022.coling-1.222.pdf) 192 | - PCBERT: Parent and Child BERT for Chinese Few-shot NER 193 | [PDF](https://aclanthology.org/2022.coling-1.192.pdf) 194 | - Adaptive Threshold Selective Self-Attention for Chinese NER 195 | [PDF](https://aclanthology.org/2022.coling-1.157.pdf) 196 | - Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER 197 | - Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes 198 | - Two Languages Are Better Than One: Bilingual Enhancement For Chinese Named Entity Recognition 199 | [PDF](https://aclanthology.org/2022.coling-1.176.pdf) 200 | - Flat Multi-modal Interaction Transformer for Named Entity Recognition 201 | - Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing 202 | - Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature 203 | - Simple yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition 204 | - Natural Annotation and Curriculum Learning to Named Entity Recognition for Under-Resourced Languages 205 | - AnonymousDataset: A Large-scale Multilingual dataset for Complex Named Entity Recognition 206 | - SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition 207 | - FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition 208 | - A Data-driven Approach to Named Entity Recognition for Early Modern French 209 | 210 | ## AAAI 2022 211 | - Unified Named Entity Recognition as Word-Word Relation Classification 212 | [PDF](https://ojs.aaai.org/index.php/AAAI/article/view/21344) 213 | 214 | ## LREC 2022 215 | - What do we really know about State of the Art NER? 216 | [PDF](https://aclanthology.org/2022.lrec-1.643/) 217 | [CODE](https://github.com/nishkalavallabhi/SOTANER) 218 | 219 | ## 其它 220 | - An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition 221 | [PDF](https://arxiv.org/abs/2208.04534) 222 | [CODE](https://github.com/yhcc/CNN_Nested_NER) 223 | 224 | # 关系抽取 225 | ## AAAI 2023 226 | - Exploring Self-distillation based Relational Reasoning Training for Document-level Relation Extraction 227 | 228 | 229 | ## ACL 2022 230 | - Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation 231 | [PDF](https://arxiv.org/pdf/2203.10900) 232 | [CODE](https://github.com/tonytan48/KD-DocRE) 233 | - RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction 234 | [PDF](https://arxiv.org/pdf/2203.09101) 235 | [CODE](https://github.com/declare-lab/RelationPrompt) 236 | - Packed Levitated Marker for Entity and Relation Extraction 237 | [PDF](https://aclanthology.org/2022.acl-long.337/) 238 | [CODE](https://github.com/ thunlp/PL-Marker) 239 | - DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction 240 | [PDF](https://arxiv.org/pdf/2104.08655) 241 | - Consistent Representation Learning for Continual Relation Extraction 242 | [PDF](https://arxiv.org/pdf/2203.02721) 243 | - Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction 244 | [PDF](https://arxiv.org/pdf/2203.10316) 245 | - Pre-training to Match for Unified Low-shot Relation Extraction 246 | [PDF](https://arxiv.org/pdf/2203.12274) 247 | - HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction 248 | [PDF](https://arxiv.org/pdf/2202.13352) 249 | - PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction 250 | [PDF](https://aclanthology.org/2022.acl-short.38/) 251 | - Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion 252 | [PDF](https://aclanthology.org/2022.findings-acl.23/) 253 | - Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation 254 | [PDF](https://arxiv.org/pdf/2203.02135) 255 | - A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction 256 | [PDF](https://arxiv.org/pdf/2205.09536) 257 | - Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition 258 | [PDF](https://aclanthology.org/2022.findings-acl.256/) 259 | - Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking 260 | [PDF](https://aclanthology.org/2022.findings-acl.147/) 261 | 262 | ## EMNLP 2022 263 | - DORE: Document Ordered Relation Extraction based on Generative Framework 264 | - CrossRE: A Cross-Domain Dataset for Relation Extraction 265 | - Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method 266 | - Named Entity and Relation Extraction with Multi-Modal Retrieval 267 | - Explore Unsupervised Structures in Pretrained Models for Relation Extraction 268 | - Summarization as Indirect Supervision for Relation Extraction 269 | - Graph-based Model Generation for Few-Shot Relation Extraction 270 | - ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select 271 | - MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction 272 | - Fine-grained Contrastive Learning for Relation Extraction 273 | - Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction 274 | - A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling 275 | - Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation 276 | - Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction 277 | - Better Few-Shot Relation Extraction with Label Prompt Dropout 278 | - MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering 279 | - Towards Better Document-level Relation Extraction via Iterative Inference 280 | - Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study 281 | - Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling 282 | 283 | ## COLING 2022 284 | - Finding Influential Instances for Distantly Supervised Relation Extraction 285 | - DCT-Centered Temporal Relation Extraction 286 | - STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction 287 | - Improving Continual Relation Extraction through Prototypical Contrastive Learning 288 | - Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest 289 | - A Hybrid Model of Classification and Generation for Spatial Relation Extraction 290 | - Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning 291 | - A Relation Extraction Dataset for Knowledge Extraction from Web Tables 292 | - MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction 293 | - RSGT: Relational Structure Guided Temporal Relation Extraction 294 | - DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction 295 | - Key Mention Pairs Guided Document-Level Relation Extraction 296 | - CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction 297 | - Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning 298 | 299 | ## NAACL 2022 300 | - HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction 301 | [PDF](https://arxiv.org/pdf/2205.02225) 302 | [CODE](https://github.com/THU-BPM/HiURE) 303 | - Few-Shot Document-Level Relation Extraction 304 | [PDF](https://arxiv.org/pdf/2205.02048) 305 | [CODE](https://github.com/nicpopovic/FREDo) 306 | - Modeling Multi-Granularity Hierarchical Features for Relation Extraction 307 | [PDF](https://arxiv.org/pdf/2204.04437) 308 | [CODE](https://github.com/xnliang98/sms) 309 | - Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis 310 | [PDF](https://arxiv.org/pdf/2205.03784) 311 | [CODE](https://github.com/vanoracai/CoRE) 312 | - Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction 313 | [PDF](https://arxiv.org/pdf/2205.03521) 314 | [CODE](https://github.com/zjunlp/HVPNeT) 315 | - Generic and Trend-aware Curriculum Learning for Relation Extraction 316 | [PDF](https://aclanthology.org/2022.naacl-main.160.pdf) 317 | - Document-Level Relation Extraction with Sentences Importance Estimation and Focusing 318 | [PDF](https://arxiv.org/pdf/2204.12679) 319 | - SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction 320 | [PDF](https://arxiv.org/pdf/2109.12093) 321 | - Modeling Explicit Task Interactions in Document-Level Joint Entity and Relation Extraction 322 | [PDF](https://arxiv.org/pdf/2205.01909) 323 | - Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction 324 | [PDF](https://arxiv.org/pdf/2205.14393) 325 | - EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction 326 | [PDF](https://aclanthology.org/2022.naacl-main.48.pdf) 327 | - A Dataset for N-ary Relation Extraction of Drug Combinations 328 | [PDF](https://arxiv.org/pdf/2205.02289) 329 | - RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction 330 | [PDF](https://aclanthology.org/2022.findings-naacl.188.pdf) 331 | - Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration 332 | [PDF](https://aclanthology.org/2022.findings-naacl.186.pdf) 333 | - Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction 334 | [PDF](https://aclanthology.org/2022.findings-naacl.139.pdf) 335 | - GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction 336 | [PDF](https://aclanthology.org/2022.findings-naacl.139.pdf) 337 | - Dependency Position Encoding for Relation Extraction 338 | [PDF](https://aclanthology.org/2022.findings-naacl.120.pdf) 339 | - Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision 340 | [PDF](https://arxiv.org/pdf/2109.09036) 341 | - Extracting Temporal Event Relation with Syntax-guided Graph Transformer 342 | [PDF](https://aclanthology.org/2022.findings-naacl.29.pdf) 343 | 344 | # www 2022 345 | - KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction 346 | [PDF](https://arxiv.org/pdf/2104.07650) 347 | [CODE](https://github.com/zjunlp/KnowPrompt) 348 | 349 | # 事件抽取 350 | ## ACL 2022 351 | - Dynamic Prefix-Tuning for Generative Template-based Event Extraction 352 | - Legal Judgment Prediction via Event Extraction with Constraints 353 | - 354 | 355 | ## EMNLP 2022 356 | - Efficient Zero-shot Event Extraction with Context-Definition Alignment 357 | - PHEE: A Dataset for Pharmacovigilance Event Extraction from Text 358 | - Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset 359 | - MEE: A Novel Multilingual Event Extraction Dataset 360 | - Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction 361 | - Open-Vocabulary Argument Role Prediction For Event Extraction 362 | - Efficient Zero-shot Event Extraction with Context-Definition Alignment 363 | - PHEE: A Dataset for Pharmacovigilance Event Extraction from Text 364 | - Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset 365 | 366 | ## COLING 2022 367 | - OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction 368 | - CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction 369 | 370 | ## NAACL 2022 371 | - RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction 372 | - DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction 373 | - DEGREE: A Data-Efficient Generation-Based Event Extraction Model 374 | 375 | # 中文NER 376 | - [ACL2021] MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition 377 | [PDF](https://arxiv.org/abs/2107.05418) 378 | [CODE](https://github.com/CoderMusou/MECT4CNER) 379 | - FGN: Fusion glyph network for Chinese named entity recognition 380 | [PDF](https://arxiv.org/pdf/2001.05272) 381 | [CODE](https://github.com/AidenHuen/FGN-NER) 382 | - fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP 383 | [PDF](https://arxiv.org/abs/2009.08633) 384 | [CODE](https://github.com/fastnlp/fastHan) 385 | - CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation 386 | [PDF](https://arxiv.org/pdf/2109.05729.pdf) 387 | [CODE](https://github.com/fastnlp/CPT) 388 | - A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records 389 | [PDF](https://www.hindawi.com/journals/complexity/2021/6631837/) 390 | - Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM-CRF 391 | [PDF](https://www.hindawi.com/journals/wcmc/2021/6696205/) 392 | --------------------------------------------------------------------------------