{"url":"/sota/relation-extraction-on-docred","task":{"name":"Relation Extraction","url":"/task/relation-extraction","note":null},"dataset":{"name":"DocRED","url":"/dataset/docred"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"**Relation Extraction** is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.\r\n\r\n\r\n<span class=\"description-source\">Source: [Deep Residual Learning for Weakly-Supervised Relation Extraction ](https://arxiv.org/abs/1707.08866)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["F1","Ign F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1":"higher","Ign F1":"higher"}},"counts":{"rows":62,"rows_with_code":50,"rows_with_paper_page":62,"rows_dated":60,"rows_using_additional_data":1},"rows":[{"rank_in_archive_order":1,"model":"DREEAM","metrics":{"F1":"67.53","Ign F1":"65.47"},"uses_additional_data":false,"paper_date":"2023-02-17","paper":"/paper/dreeam-guiding-attention-with-evidence-for","paper_url":"https://arxiv.org/abs/2302.08675v1","paper_title":"DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction","code":"https://github.com/youmima/dreeam","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"KD-Rb-l","metrics":{"F1":"67.28","Ign F1":"65.24"},"uses_additional_data":false,"paper_date":"2022-03-21","paper":"/paper/document-level-relation-extraction-with-4","paper_url":"https://arxiv.org/abs/2203.10900v1","paper_title":"Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation","code":"https://github.com/tonytan48/kd-docre","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"SSAN-RoBERTa-large+Adaptation","metrics":{"F1":"65.92","Ign F1":"63.78"},"uses_additional_data":false,"paper_date":"2021-02-20","paper":"/paper/entity-structure-within-and-throughout","paper_url":"https://arxiv.org/abs/2102.10249v1","paper_title":"Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction","code":"https://github.com/PaddlePaddle/Research","n_code_links":3,"syntology":null},{"rank_in_archive_order":4,"model":"SAIS-RoBERTa-large","metrics":{"F1":"65.11","Ign F1":"63.44"},"uses_additional_data":false,"paper_date":"2021-09-24","paper":"/paper/sais-supervising-and-augmenting-intermediate","paper_url":"https://arxiv.org/abs/2109.12093v2","paper_title":"SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction","code":"https://github.com/xiaoyuxin1002/sais","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"Eider-RoBERTa-large","metrics":{"F1":"64.79","Ign F1":"62.85"},"uses_additional_data":false,"paper_date":"2021-06-16","paper":"/paper/eider-evidence-enhanced-document-level","paper_url":"https://arxiv.org/abs/2106.08657v2","paper_title":"Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion","code":"https://github.com/veronicium/eider","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"DocuNet-RoBERTa-large","metrics":{"F1":"64.55","Ign F1":"62.4"},"uses_additional_data":false,"paper_date":"2021-06-07","paper":"/paper/document-level-relation-extraction-as","paper_url":"https://arxiv.org/abs/2106.03618v2","paper_title":"Document-level Relation Extraction as Semantic Segmentation","code":"https://github.com/zjunlp/DocuNet","n_code_links":2,"syntology":null},{"rank_in_archive_order":7,"model":"CGM2IR-RoBERTalarge","metrics":{"F1":"63.89","Ign F1":"61.96"},"uses_additional_data":false,"paper_date":"2022-01-13","paper":"/paper/document-level-relation-extraction-with-2","paper_url":"https://arxiv.org/abs/2201.04826v1","paper_title":"Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":8,"model":"SETE-Roberta-large","metrics":{"F1":"63.74","Ign F1":"61.78"},"uses_additional_data":false,"paper_date":"2022-07-11","paper":"/paper/document-level-relation-extraction-with-6","paper_url":"https://ieeexplore.ieee.org/abstract/document/9892647","paper_title":"Document-Level Relation Extraction with Structure Enhanced Transformer Encoder","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":9,"model":"ATLOP-RoBERTa-large","metrics":{"F1":"63.40","Ign F1":"61.39"},"uses_additional_data":false,"paper_date":"2020-10-21","paper":"/paper/document-level-relation-extraction-with","paper_url":"https://arxiv.org/abs/2010.11304v3","paper_title":"Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling","code":"https://github.com/wzhouad/ATLOP","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":10,"model":"DRE-MIR-BERTbase","metrics":{"F1":"63.15","Ign F1":"61.03"},"uses_additional_data":false,"paper_date":"2022-04-21","paper":"/paper/a-masked-image-reconstruction-network-for","paper_url":"https://arxiv.org/abs/2204.09851v2","paper_title":"A Masked Image Reconstruction Network for Document-level Relation Extraction","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":11,"model":"SAIS-BERT-base","metrics":{"F1":"62.77","Ign F1":"60.96"},"uses_additional_data":false,"paper_date":"2021-09-24","paper":"/paper/sais-supervising-and-augmenting-intermediate","paper_url":"https://arxiv.org/abs/2109.12093v2","paper_title":"SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction","code":"https://github.com/xiaoyuxin1002/sais","n_code_links":1,"syntology":null},{"rank_in_archive_order":12,"model":"GAIN-BERT-large","metrics":{"F1":"62.76","Ign F1":"60.31"},"uses_additional_data":false,"paper_date":"2020-09-29","paper":"/paper/double-graph-based-reasoning-for-document","paper_url":"https://arxiv.org/abs/2009.13752v1","paper_title":"Double Graph Based Reasoning for Document-level Relation Extraction","code":"https://github.com/DreamInvoker/GAIN","n_code_links":2,"syntology":null},{"rank_in_archive_order":13,"model":"Dense-CCNet-BERTbase","metrics":{"F1":"62.55","Ign F1":"60.46"},"uses_additional_data":false,"paper_date":"2022-03-26","paper":"/paper/a-densely-connected-criss-cross-attention","paper_url":"https://arxiv.org/abs/2203.13953v1","paper_title":"A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":14,"model":"E2GRE-RoBERTa-large","metrics":{"F1":"62.50","Ign F1":"60.30"},"uses_additional_data":false,"paper_date":"2020-08-27","paper":"/paper/entity-and-evidence-guided-relation","paper_url":"https://arxiv.org/abs/2008.12283v1","paper_title":"Entity and Evidence Guided Relation Extraction for DocRED","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":15,"model":"Eider-BERT-base","metrics":{"F1":"62.47","Ign F1":"60.42"},"uses_additional_data":false,"paper_date":"2021-06-16","paper":"/paper/eider-evidence-enhanced-document-level","paper_url":"https://arxiv.org/abs/2106.08657v2","paper_title":"Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion","code":"https://github.com/veronicium/eider","n_code_links":1,"syntology":null},{"rank_in_archive_order":16,"model":"SagDRE","metrics":{"F1":"62.32","Ign F1":"60.11"},"uses_additional_data":false,"paper_date":"2021-11-16","paper":"/paper/sagdre-sequence-aware-graph-based-document","paper_url":"https://openreview.net/forum?id=Vi9Cj61ZGsR","paper_title":"SagDRE: Sequence-Aware Graph-Based Document-Level Relation Extraction with Adaptive Margin Loss","code":"https://github.com/IAmHedgehog/SagDRE","n_code_links":1,"syntology":null},{"rank_in_archive_order":17,"model":"GAIN+SIEF","metrics":{"F1":"62.29","Ign F1":"59.87"},"uses_additional_data":false,"paper_date":"2022-04-27","paper":"/paper/document-level-relation-extraction-with-3","paper_url":"https://arxiv.org/abs/2204.12679v1","paper_title":"Document-Level Relation Extraction with Sentences Importance Estimation and Focusing","code":"https://github.com/xwjim/sief","n_code_links":1,"syntology":null},{"rank_in_archive_order":18,"model":"CGM2IR-BERTbase","metrics":{"F1":"62.06","Ign F1":"60.24"},"uses_additional_data":false,"paper_date":"2022-01-13","paper":"/paper/document-level-relation-extraction-with-2","paper_url":"https://arxiv.org/abs/2201.04826v1","paper_title":"Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":19,"model":"SIRE-BERT-base","metrics":{"F1":"62.05","Ign F1":"60.18"},"uses_additional_data":false,"paper_date":"2021-06-03","paper":"/paper/sire-separate-intra-and-inter-sentential","paper_url":"https://arxiv.org/abs/2106.01709v1","paper_title":"SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction","code":"https://github.com/DreamInvoker/SIRE","n_code_links":1,"syntology":null},{"rank_in_archive_order":20,"model":"MRN+BERT","metrics":{"F1":"61.74","Ign F1":"59.52"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/mrn-a-locally-and-globally-mention-based","paper_url":"https://aclanthology.org/2021.findings-acl.117","paper_title":"MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction","code":"https://github.com/ljynlp/mrn","n_code_links":1,"syntology":null},{"rank_in_archive_order":21,"model":"ATLOP + LogiRE","metrics":{"F1":"61.45","Ign F1":"59.48"},"uses_additional_data":false,"paper_date":"2021-11-09","paper":"/paper/learning-logic-rules-for-document-level-1","paper_url":"https://arxiv.org/abs/2111.05407v1","paper_title":"Learning Logic Rules for Document-level Relation Extraction","code":"https://github.com/rudongyu/logire","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":3,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":22,"model":"SSAN-RoBERTa-large","metrics":{"F1":"61.42","Ign F1":"59.47"},"uses_additional_data":false,"paper_date":"2021-02-20","paper":"/paper/entity-structure-within-and-throughout","paper_url":"https://arxiv.org/abs/2102.10249v1","paper_title":"Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction","code":"https://github.com/PaddlePaddle/Research","n_code_links":3,"syntology":null},{"rank_in_archive_order":23,"model":"ATLOP + KIRE","metrics":{"F1":"61.39","Ign F1":"59.35"},"uses_additional_data":false,"paper_date":"2022-07-23","paper":"/paper/enhancing-document-level-relation-extraction","paper_url":"https://arxiv.org/abs/2207.11433v1","paper_title":"Enhancing Document-level Relation Extraction by Entity Knowledge Injection","code":"https://github.com/nju-websoft/kire","n_code_links":1,"syntology":null},{"rank_in_archive_order":24,"model":"DRN-BERT-base","metrics":{"F1":"61.37","Ign F1":"59.15"},"uses_additional_data":false,"paper_date":"2021-06-03","paper":"/paper/discriminative-reasoning-for-document-level","paper_url":"https://arxiv.org/abs/2106.01562v1","paper_title":"Discriminative Reasoning for Document-level Relation Extraction","code":"https://github.com/xwjim/DRN","n_code_links":2,"syntology":{"n_ran":8,"n_unverified":2,"n_samples":10,"n_pointer_only_licence":0}},{"rank_in_archive_order":25,"model":"ERACL","metrics":{"F1":"61.36","Ign F1":"59.08"},"uses_additional_data":false,"paper_date":"2022-05-21","paper":"/paper/improving-long-tailed-document-level-relation","paper_url":"https://arxiv.org/abs/2205.10511v1","paper_title":"Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":26,"model":"ATLOP-BERT-base","metrics":{"F1":"61.30","Ign F1":"59.31"},"uses_additional_data":false,"paper_date":"2020-10-21","paper":"/paper/document-level-relation-extraction-with","paper_url":"https://arxiv.org/abs/2010.11304v3","paper_title":"Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling","code":"https://github.com/wzhouad/ATLOP","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":27,"model":"GAIN-BERT","metrics":{"F1":"61.24","Ign F1":"59.00"},"uses_additional_data":false,"paper_date":"2020-09-29","paper":"/paper/double-graph-based-reasoning-for-document","paper_url":"https://arxiv.org/abs/2009.13752v1","paper_title":"Double Graph Based Reasoning for Document-level Relation Extraction","code":"https://github.com/DreamInvoker/GAIN","n_code_links":2,"syntology":null},{"rank_in_archive_order":28,"model":"CorefDRE- BERT","metrics":{"F1":"60.82","Ign F1":"60.78"},"uses_additional_data":false,"paper_date":"2022-02-22","paper":"/paper/corefdre-document-level-relation-extraction","paper_url":"https://arxiv.org/abs/2202.10744v1","paper_title":"CorefDRE: Document-level Relation Extraction with coreference resolution","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":29,"model":"GAIN + LogiRE","metrics":{"F1":"60.61","Ign F1":"58.62"},"uses_additional_data":false,"paper_date":"2021-11-09","paper":"/paper/learning-logic-rules-for-document-level-1","paper_url":"https://arxiv.org/abs/2111.05407v1","paper_title":"Learning Logic Rules for Document-level Relation Extraction","code":"https://github.com/rudongyu/logire","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":3,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":30,"model":"JEREX-BERT-base","metrics":{"F1":"60.40","Ign F1":"58.44"},"uses_additional_data":false,"paper_date":"2021-02-11","paper":"/paper/an-end-to-end-model-for-entity-level-relation","paper_url":"https://arxiv.org/abs/2102.05980v2","paper_title":"An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning","code":"https://github.com/lavis-nlp/jerex","n_code_links":1,"syntology":null},{"rank_in_archive_order":31,"model":"CorefRoBERTa-large","metrics":{"F1":"60.25","Ign F1":"57.90"},"uses_additional_data":false,"paper_date":"2020-04-15","paper":"/paper/coreferential-reasoning-learning-for-language","paper_url":"https://arxiv.org/abs/2004.06870v2","paper_title":"Coreferential Reasoning Learning for Language Representation","code":"https://github.com/thunlp/KernelGAT","n_code_links":2,"syntology":null},{"rank_in_archive_order":32,"model":"MIUK","metrics":{"F1":"59.99","Ign F1":"58.05"},"uses_additional_data":true,"paper_date":"2021-04-28","paper":"/paper/multi-view-inference-for-relation-extraction","paper_url":"https://arxiv.org/abs/2104.13579v1","paper_title":"Multi-view Inference for Relation Extraction with Uncertain Knowledge","code":"https://github.com/pkuserc/AAAI2021-MIUK-Relation-Extraction","n_code_links":1,"syntology":null},{"rank_in_archive_order":33,"model":"SSAN-RoBERTa-base","metrics":{"F1":"59.94","Ign F1":"57.71"},"uses_additional_data":false,"paper_date":"2021-02-20","paper":"/paper/entity-structure-within-and-throughout","paper_url":"https://arxiv.org/abs/2102.10249v1","paper_title":"Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction","code":"https://github.com/PaddlePaddle/Research","n_code_links":3,"syntology":null},{"rank_in_archive_order":34,"model":"CFER-BERT-base","metrics":{"F1":"59.82","Ign F1":"57.89"},"uses_additional_data":false,"paper_date":"2020-12-04","paper":"/paper/coarse-to-fine-entity-representations-for","paper_url":"https://arxiv.org/abs/2012.02507v2","paper_title":"Coarse-to-Fine Entity Representations for Document-level Relation Extraction","code":"https://github.com/Hunter-DDM/cfer-document-level-RE","n_code_links":1,"syntology":null},{"rank_in_archive_order":35,"model":"HeterGSAN+Reconstruction+BERT-base","metrics":{"F1":"59.45","Ign F1":"57.12"},"uses_additional_data":false,"paper_date":"2020-12-21","paper":"/paper/document-level-relation-extraction-with-1","paper_url":"https://arxiv.org/abs/2012.11384v1","paper_title":"Document-Level Relation Extraction with Reconstruction","code":"https://github.com/xwjim/DocRE-Rec","n_code_links":1,"syntology":null},{"rank_in_archive_order":36,"model":"FineCL","metrics":{"F1":"59.4","Ign F1":"57.1"},"uses_additional_data":false,"paper_date":"2022-05-25","paper":"/paper/fine-grained-contrastive-learning-for","paper_url":"https://arxiv.org/abs/2205.12491v2","paper_title":"Fine-grained Contrastive Learning for Relation Extraction","code":"https://github.com/david-li0406/read","n_code_links":1,"syntology":null},{"rank_in_archive_order":37,"model":"SSAN+RSMAN","metrics":{"F1":"59.29","Ign F1":"57.02"},"uses_additional_data":false,"paper_date":"2022-05-28","paper":"/paper/relation-specific-attentions-over-entity","paper_url":"https://arxiv.org/abs/2205.14393v1","paper_title":"Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction","code":"https://github.com/fduyjx/rsman","n_code_links":1,"syntology":null},{"rank_in_archive_order":38,"model":"LSR+BERT-base","metrics":{"F1":"59.05","Ign F1":"56.97"},"uses_additional_data":false,"paper_date":"2020-05-13","paper":"/paper/reasoning-with-latent-structure-refinement","paper_url":"https://arxiv.org/abs/2005.06312v3","paper_title":"Reasoning with Latent Structure Refinement for Document-Level Relation Extraction","code":"https://github.com/nanguoshun/LSR","n_code_links":2,"syntology":{"n_ran":5,"n_unverified":2,"n_samples":7,"n_pointer_only_licence":6}},{"rank_in_archive_order":39,"model":"GLRE-XLNet-Large","metrics":{"F1":"59.0","Ign F1":"56.8"},"uses_additional_data":false,"paper_date":"2020-09-22","paper":"/paper/global-to-local-neural-networks-for-document","paper_url":"https://arxiv.org/abs/2009.10359v1","paper_title":"Global-to-Local Neural Networks for Document-Level Relation Extraction","code":"https://github.com/nju-websoft/GLRE","n_code_links":1,"syntology":null},{"rank_in_archive_order":40,"model":"CorefBERT-large","metrics":{"F1":"58.83","Ign F1":"56.40"},"uses_additional_data":false,"paper_date":"2020-04-15","paper":"/paper/coreferential-reasoning-learning-for-language","paper_url":"https://arxiv.org/abs/2004.06870v2","paper_title":"Coreferential Reasoning Learning for Language Representation","code":"https://github.com/thunlp/KernelGAT","n_code_links":2,"syntology":null},{"rank_in_archive_order":41,"model":"E2GRE-BERT-base","metrics":{"F1":"58.72","Ign F1":"55.22"},"uses_additional_data":false,"paper_date":"2020-08-27","paper":"/paper/entity-and-evidence-guided-relation","paper_url":"https://arxiv.org/abs/2008.12283v1","paper_title":"Entity and Evidence Guided Relation Extraction for DocRED","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":42,"model":"EncAttAgg","metrics":{"F1":"58.7","Ign F1":"56.7"},"uses_additional_data":false,"paper_date":"2020-08-09","paper":"/paper/improving-document-level-relation-extraction","paper_url":"https://ieeexplore.ieee.org/abstract/document/9194547","paper_title":"Improving Document-level Relation Extraction via Contextualizing Mention Representations and Weighting Mention Pairs","code":"https://github.com/nefujiangping/EncAttAgg","n_code_links":1,"syntology":null},{"rank_in_archive_order":43,"model":"MRN","metrics":{"F1":"58.46","Ign F1":"56.19"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/mrn-a-locally-and-globally-mention-based","paper_url":"https://aclanthology.org/2021.findings-acl.117","paper_title":"MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction","code":"https://github.com/ljynlp/mrn","n_code_links":1,"syntology":null},{"rank_in_archive_order":44,"model":"SSAN-BERT-base","metrics":{"F1":"58.16","Ign F1":"55.84"},"uses_additional_data":false,"paper_date":"2021-02-20","paper":"/paper/entity-structure-within-and-throughout","paper_url":"https://arxiv.org/abs/2102.10249v1","paper_title":"Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction","code":"https://github.com/PaddlePaddle/Research","n_code_links":3,"syntology":null},{"rank_in_archive_order":45,"model":"DUAL+BERT-base","metrics":{"F1":"57.74"},"uses_additional_data":false,"paper_date":"2020-11-24","paper":"/paper/dual-supervision-framework-for-relation","paper_url":"https://arxiv.org/abs/2011.11851v1","paper_title":"Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation","code":"https://github.com/woohwanjung/dual","n_code_links":1,"syntology":null},{"rank_in_archive_order":46,"model":"CorefBERT-base","metrics":{"F1":"56.96","Ign F1":"54.54"},"uses_additional_data":false,"paper_date":"2020-04-15","paper":"/paper/coreferential-reasoning-learning-for-language","paper_url":"https://arxiv.org/abs/2004.06870v2","paper_title":"Coreferential Reasoning Learning for Language Representation","code":"https://github.com/thunlp/KernelGAT","n_code_links":2,"syntology":null},{"rank_in_archive_order":47,"model":"DRN-GloVe","metrics":{"F1":"56.33","Ign F1":"54.35"},"uses_additional_data":false,"paper_date":"2021-06-03","paper":"/paper/discriminative-reasoning-for-document-level","paper_url":"https://arxiv.org/abs/2106.01562v1","paper_title":"Discriminative Reasoning for Document-level Relation Extraction","code":"https://github.com/xwjim/DRN","n_code_links":2,"syntology":{"n_ran":8,"n_unverified":2,"n_samples":10,"n_pointer_only_licence":0}},{"rank_in_archive_order":48,"model":"Paths+BiLSTM-GloVe","metrics":{"F1":"56.23"},"uses_additional_data":false,"paper_date":"2021-06-03","paper":"/paper/three-sentences-are-all-you-need-local-path","paper_url":"https://arxiv.org/abs/2106.01793v1","paper_title":"Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction","code":"https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":49,"model":"SIRE-GloVe","metrics":{"F1":"55.96","Ign F1":"54.04"},"uses_additional_data":false,"paper_date":"2021-06-03","paper":"/paper/sire-separate-intra-and-inter-sentential","paper_url":"https://arxiv.org/abs/2106.01709v1","paper_title":"SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction","code":"https://github.com/DreamInvoker/SIRE","n_code_links":1,"syntology":null},{"rank_in_archive_order":50,"model":"CFER-GloVe","metrics":{"F1":"55.75","Ign F1":"53.43"},"uses_additional_data":false,"paper_date":"2020-12-04","paper":"/paper/coarse-to-fine-entity-representations-for","paper_url":"https://arxiv.org/abs/2012.02507v2","paper_title":"Coarse-to-Fine Entity Representations for Document-level Relation Extraction","code":"https://github.com/Hunter-DDM/cfer-document-level-RE","n_code_links":1,"syntology":null},{"rank_in_archive_order":51,"model":"HIN-BERT-base","metrics":{"F1":"55.60","Ign F1":"53.70"},"uses_additional_data":false,"paper_date":"2020-03-28","paper":"/paper/hin-hierarchical-inference-network-for","paper_url":"https://arxiv.org/abs/2003.12754v1","paper_title":"HIN: Hierarchical Inference Network for Document-Level Relation Extraction","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":52,"model":"HeterGSAN+Reconstruction","metrics":{"F1":"55.23","Ign F1":"53.27"},"uses_additional_data":false,"paper_date":"2020-12-21","paper":"/paper/document-level-relation-extraction-with-1","paper_url":"https://arxiv.org/abs/2012.11384v1","paper_title":"Document-Level Relation Extraction with Reconstruction","code":"https://github.com/xwjim/DocRE-Rec","n_code_links":1,"syntology":null},{"rank_in_archive_order":53,"model":"GAIN-GloVe","metrics":{"F1":"55.08","Ign F1":"52.66"},"uses_additional_data":false,"paper_date":"2020-09-29","paper":"/paper/double-graph-based-reasoning-for-document","paper_url":"https://arxiv.org/abs/2009.13752v1","paper_title":"Double Graph Based Reasoning for Document-level Relation Extraction","code":"https://github.com/DreamInvoker/GAIN","n_code_links":2,"syntology":null},{"rank_in_archive_order":54,"model":"LSR+GloVe","metrics":{"F1":"54.18","Ign F1":"52.15"},"uses_additional_data":false,"paper_date":"2020-05-13","paper":"/paper/reasoning-with-latent-structure-refinement","paper_url":"https://arxiv.org/abs/2005.06312v3","paper_title":"Reasoning with Latent Structure Refinement for Document-Level Relation Extraction","code":"https://github.com/nanguoshun/LSR","n_code_links":2,"syntology":{"n_ran":5,"n_unverified":2,"n_samples":7,"n_pointer_only_licence":6}},{"rank_in_archive_order":55,"model":"Two-Step+BERT-base","metrics":{"F1":"53.92","Ign F1":"54.42"},"uses_additional_data":false,"paper_date":"2019-09-26","paper":"/paper/fine-tune-bert-for-docred-with-two-step","paper_url":"https://arxiv.org/abs/1909.11898v1","paper_title":"Fine-tune Bert for DocRED with Two-step Process","code":"https://github.com/hongwang600/DocRed","n_code_links":1,"syntology":null},{"rank_in_archive_order":56,"model":"HIN-GloVe","metrics":{"F1":"53.30","Ign F1":"51.15"},"uses_additional_data":false,"paper_date":"2020-03-28","paper":"/paper/hin-hierarchical-inference-network-for","paper_url":"https://arxiv.org/abs/2003.12754v1","paper_title":"HIN: Hierarchical Inference Network for Document-Level Relation Extraction","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":57,"model":"BERT-base","metrics":{"F1":"53.22","Ign F1":"56.17"},"uses_additional_data":false,"paper_date":"2019-09-26","paper":"/paper/fine-tune-bert-for-docred-with-two-step","paper_url":"https://arxiv.org/abs/1909.11898v1","paper_title":"Fine-tune Bert for DocRED with Two-step Process","code":"https://github.com/hongwang600/DocRed","n_code_links":1,"syntology":null},{"rank_in_archive_order":58,"model":"GREG","metrics":{"F1":"52.88"},"uses_additional_data":false,"paper_date":"2020-02-10","paper":"/paper/greg-a-global-level-relation-extraction-with","paper_url":"https://www.mdpi.com/2076-3417/10/3/1181/htm","paper_title":"GREG: A Global Level Relation Extraction with Knowledge Graph Embedding","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":59,"model":"BiLSTM","metrics":{"F1":"51.06","Ign F1":"44.73"},"uses_additional_data":false,"paper_date":"2019-06-14","paper":"/paper/docred-a-large-scale-document-level-relation","paper_url":"https://arxiv.org/abs/1906.06127v3","paper_title":"DocRED: A Large-Scale Document-Level Relation Extraction Dataset","code":"https://github.com/thunlp/DocRED","n_code_links":4,"syntology":null},{"rank_in_archive_order":60,"model":"DocRED-Context-Aware","metrics":{"F1":"50.64","Ign F1":"43.93"},"uses_additional_data":false,"paper_date":"2019-06-14","paper":"/paper/docred-a-large-scale-document-level-relation","paper_url":"https://arxiv.org/abs/1906.06127v3","paper_title":"DocRED: A Large-Scale Document-Level Relation Extraction Dataset","code":"https://github.com/thunlp/DocRED","n_code_links":4,"syntology":null},{"rank_in_archive_order":61,"model":"BiLSTM","metrics":{"F1":"50.12","Ign F1":"43.60"},"uses_additional_data":false,"paper_date":"2019-06-14","paper":"/paper/docred-a-large-scale-document-level-relation","paper_url":"https://arxiv.org/abs/1906.06127v3","paper_title":"DocRED: A Large-Scale Document-Level Relation Extraction Dataset","code":"https://github.com/thunlp/DocRED","n_code_links":4,"syntology":null},{"rank_in_archive_order":62,"model":"DocRED-CNN","metrics":{"F1":"42.33","Ign F1":"36.44"},"uses_additional_data":false,"paper_date":"2019-06-14","paper":"/paper/docred-a-large-scale-document-level-relation","paper_url":"https://arxiv.org/abs/1906.06127v3","paper_title":"DocRED: A Large-Scale Document-Level Relation Extraction Dataset","code":"https://github.com/thunlp/DocRED","n_code_links":4,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":9,"rows_with_any_sample_ran":7,"distinct_papers_with_graph_line":5,"distinct_papers_with_any_sample_ran":4,"samples_over_distinct_papers":{"n_ran":18,"n_unverified":7,"n_samples":25,"n_pointer_only_licence":8,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":33,"n_unverified":14,"n_samples":47,"n_pointer_only_licence":16,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}