Information Extraction Papers Collections

• More Details about this Topic

  • GitHub: gkiril/oie-resources: A curated list of Open Information Extraction (OIE) - resources: papers, code, data, etc.

  • GitHub: https://github.com/thunlp/NREPapers

Datasets

• More Resource about this Topic
  • GitHub: IE-Datasets-Collections
  • GitHub: IE Dataset Zoo

[Sci Related]

• [Sci] Overview of the First Workshop on Scholarly Document Processing(SDP)
• [Sci] Overview of the Second Workshop on Scholarly Document Processing(SDP)

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• [NAACL18] PeerRead: A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
• [EMNLP18] SciERC: Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
• [ACL20] SciREX: SciREX: A Challenge Dataset for Document-Level Information Extraction
• [LERCXX🙏] SciCN:
• [EMNLP20] SCIFACT Fact or Fiction: Verifying Scientific Claims

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• [EMNLP19] SciBERT: A Pretrained Language Model for Scientific Text
• [Bioinformatics'2020]: BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• [arxiv21]OAG-BERT: Pre-train Heterogeneous Entity-augmented Academic Language Models

Named Entity Recognition

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• [ACL18]Chinese NER Using Lattice LSTM
  • https://github.com/LeeSureman/Batch_Parallel_LatticeLSTM
• [EMNLP19] Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
  • https://github.com/DianboWork/Graph4CNER
• [EMNLP19] A Lexicon-Based Graph Neural Network for Chinese NER
  • https://github.com/RowitZou/LGN
• [IJCAI19] CNN-Based Chinese NER with Lexicon Rethinking
  • https://github.com/rtmaww/LR-CNN
• [ACL20]FLAT: Chinese NER Using Flat-Lattice Transformer
  • https://github.com/LeeSureman/Flat-Lattice-Transformer
• [ACL20]Simplify the Usage of Lexicon in Chinese NER
  • https://github.com/v-mipeng/LexiconAugmentedNER
• [ X 19]TENER: Adapting Transformer Encoder for Named Entity Recognition Hang
  • https://github.com/fastnlp/TENER
• [AAAI21] Dynamic Modeling Cross- and Self-Lattice Attention Network for Chinese NER
  • https://github.com/zs50910/DCSAN-for-Chinese-NER

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• [ACL21] Lexicon Enhanced Chinese Sequence Labelling Using BERT Adapter
  • https://github.com/liuwei1206/LEBERT
• [ACL21] Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models
  • https://github.com/alibaba/AliceMind

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• [GitHUB Page]Low-resource Knowledge Extraction
  • https://github.com/zjunlp/Low-resource-KEPapers
• [ ACL19] Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
  • https://github.com/v-mipeng/LexiconNER
• [ACL20] Soft Gazetteers for Low-Resource Named Entity Recognition
  • https://github.com/shrutirij/soft-gazetteers
• [ACL20] TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
  • https://github.com/INK-USC/TriggerNER
• [ACL21] Noisy-Labeled NER with Confidence Estimation
  • https://github.com/liukun95/Noisy-NER-Confidence-Estimation
• [NAACL21] GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input
• [ACL21] Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling

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• [ACL21] Template-Based Named Entity Recognition Using BART

  • https://github.com/Nealcly/templateNER

• [ICLR21] STRUCTURED PREDICTION AS TRANSLATION BETWEEN AUGMENTED NATURAL LANGUAGES

  • https://github.com/amazon-research/tanl

• [ WWW22] KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

  • https://github.com/zjunlp/KnowPrompt

• [Sigir 2022 review] Knowledge-injected Prompt Tuning for Event Detection

  • XX

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• [ACL21]RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models
  • https://github.com/INK-USC/RockNER

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• [ACL20] A Unified MRC Framework for Named Entity Recognition
  • https://github.com/ShannonAI/mrc-for-flat-nested-ner
• [ACL21] A Unified Generative Framework for Various NER Subtasks
  • https://github.com/yhcc/BARTNER
• [IJCAI21] A Sequence-to-Set Network for Nested Named Entity Recognition
  • https://github.com/zqtan1024/sequence-to-set
• [ACL21 ] Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition
  • https://github.com/tricktreat/locate-and-label

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• [EMNLP19] A Boundary-aware Model for Nested Named Entity Recognition
  • https://github.com/thecharm/boundary-aware-nested-ner
• [ACL20] Pyramid: A Layered Model for Nested Named Entity Recognition
  • https://github.com/LorrinWWW/Pyramid
• [AAAI20] Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition
• [AAAI20] Hierarchical Contextualized Representation for Named Entity Recognition
  • https://github.com/cslydia/Hire-NER
• [AAA21]A Supervised Multi-Head Self-Attention Network for Nested Named Entity Recognition
  • https://github.com/xyxAda/Attention_NER
• [ACL21]A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
  • https://github.com/foxlf823/sodner

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• [Survey] Few-Shot Named Entity Recognition: A Comprehensive Study
• [EMNLP20] Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning
  • https://github.com/asappresearch/structshot

Relation Extraction

• [ACL19] Span-Level Model for Relation Extraction
  • https://github.com/wzhouad/RE_improved_baseline
• [EMNLP20] Learning from Context or Names? An Empirical Study on Neural Relation Extraction
  • https://github.com/thunlp/RE-Context-or-Names
• [ACL20] A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
  • https://github.com/longlongman/CasRel-pytorch-reimplement
• [CONLING20] TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
  • https://github.com/131250208/TPlinker-joint-extraction
• [EMNLP20]Relation of the Relations: A New Paradigm of the Relation Extraction Problem
  • https://github.com/FFTYYY/RoR_relation_extraction
• [AAA21]Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
  • https://github.com/wzhouad/ATLOP
• [IJCAI21]Document-level Relation Extraction as Semantic Segmentation
  • https://github.com/zjunlp/DocuNet
• [EACL21] An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
  • https://github.com/lavis-nlp/jerex
• [AAA21] Contrastive Triple Extraction with Generative Transformer.
• [NAACL21] Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network
  • xx
• [NAACL21] Macro-Average: Rare Types Are Important Too
  • https://github.com/thammegowda/007-mt-eval-macro
• X [EMNLP 2021]: A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling
  • https://github.com/neukg/GRTE
• [WSDM22] A Simple but Effective Bidirectional Framework for Relational Triple Extraction
  • https://github.com/neukg/BiRTE

Joint Entity and Relation

• [ACL14 ] Incremental Joint Extraction of Entity Mentions and Relations
• [EMNLP20]Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction!
  • https://github.com/btaille/sincere
• [EMNLP20] An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction
  • https://github.com/bhargaviparanjape/explainable_qa
• [Expert Systems With Applications 2018] Joint entity recognition and relation extraction as a multi-head selection problem
  • https://github.com/bekou/multihead_joint_entity_relation_extraction
• [EMNLP2018] Adversarial training for multi-context joint entity and relation extraction
  • https://github.com/mangonihao/MultiHeadJointEntityRelationExtraction_simple
• [ NAACL19] A General Framework for Information Extraction using Dynamic Span Graphs
  • https://github.com/luanyi/DyGIE
• [EMNLP2019] Entity, Relation, and Event Extraction with Contextualized Span Representations
  • https://github.com/dwadden/dygiepp
• [ACL20] A Joint Neural Model for Information Extraction with Global Features
  • code
• [ECAI20] Span-based Joint Entity and Relation Extraction with Transformer Pre-training
  • https://github.com/lavis-nlp/spert
• [WWW21] A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
  • https://github.com/tricktreat/trimf
• [CONLING20] Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations
• [ x] Boosting Span-based Joint Entity and Relation Extraction via Sequence Tagging Mechanism
• [EMNLP20] Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
  • https://github.com/BDBC-KG-NLP/Recurrent_Interaction_Network_EMNLP2020
• [ACL21] A Partition Filter Network for Joint Entity and Relation Extraction
  • https://github.com/Coopercoppers/PFN
• [NAACL21] A Frustratingly Easy Approach for Entity and Relation Extraction
  • https://github.com/princeton-nlp/PURE
• [X 21] Pack Together: Entity and Relation Extraction with Levitated Marker
  • https://github.com/thunlp/PL-Marker

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• [ACL20] Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
  • https://github.com/LorrinWWW/two-are-better-than-one
• [ACL21] UNIRE: A Unified Label Space for Entity Relation Extraction
  • https://github.com/Receiling/UniRE
• [ACL20] Joint Entity and Relation Extraction with Set Prediction Networks
  • https://github.com/DianboWork/SPN4RE

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• [EMNLP18] Pre-training Entity Relation Encoder with Intra-span and Inter-spanInformation
  • https://github.com/Receiling/PSPE
• [EACL21] ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction
  • https://github.com/Receiling/ENPAR
• [ACL21] ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning
  • https://github.com/thunlp/ERICA

Event Extraction

• [ACL19] Open Domain Event Extraction Using Neural Latent Variable Models