mlsanityData to 14 Oct 2026

A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods

arXiv 2310.19055 ↗First posted 29 Oct 2023cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Named Entity Recognition (NER) and Relation Classification (RC) are important steps in extracting information from unstructured text and formatting it into a machine-readable format. We present a survey of recent deep learning models that address named entity recognition and relation classification, with focus on few-shot learning performance. Our survey is helpful for researchers in knowing the recent techniques in text mining and extracting structured information from raw text.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 44 of its 74 references that are in the map. First posted on arXiv 29 Oct 2023.

Builds on

44 of 74 references.

References · 44

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 834
  4. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
  5. 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
  6. 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
  7. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
  8. 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
  9. 2019A Unified MRC Framework for Named Entity Recognitioncited by 199
  10. 2021KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extractioncited by 180
  11. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  12. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 152
32 more
  1. 2020Named Entity Recognition as Dependency Parsingcited by 151
  2. 2013Zero-Shot Learning by Convex Combination of Semantic Embeddingscited by 135
  3. 2020Reasoning with Latent Structure Refinement for Document-Level Relation Extractioncited by 97
  4. 2019FewRel 2.0: Towards More Challenging Few-Shot Relation Classificationcited by 94
  5. 2020BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervisioncited by 91
  6. 2021Unified Named Entity Recognition as Word-Word Relation Classificationcited by 90
  7. 2020Automated Concatenation of Embeddings for Structured Predictioncited by 86
  8. 2020Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Networkcited by 83
  9. 2019Attention Guided Graph Convolutional Networks for Relation Extractioncited by 82
  10. 2021Improving Named Entity Recognition by External Context Retrieving and Cooperative Learningcited by 76
  11. 2020Learning from Context or Names? An Empirical Study on Neural Relation Extractioncited by 75
  12. 2021Re-TACRED: Addressing Shortcomings of the TACRED Datasetcited by 74
  13. 2019TENER: Adapting Transformer Encoder for Named Entity Recognitioncited by 69
  14. 2021FLEX: Unifying Evaluation for Few-Shot NLPcited by 67
  15. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
  16. 2016Distant Supervision for Relation Extraction beyond the Sentence Boundarycited by 62
  17. 2021Locate and Label: A Two-stage Identifier for Nested Named Entity Recognitioncited by 61
  18. 2020More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extractioncited by 52
  19. 2021Packed Levitated Marker for Entity and Relation Extractioncited by 49
  20. 2020Named Entity Recognition without Labelled Data: A Weak Supervision Approachcited by 46
  21. 2020FLERT: Document-Level Features for Named Entity Recognitioncited by 45
  22. 2022DeepStruct: Pretraining of Language Models for Structure Predictioncited by 45
  23. 2022Autoregressive Structured Prediction with Language Modelscited by 43
  24. 2021A Sequence-to-Set Network for Nested Named Entity Recognitioncited by 40
  25. 2019Multi-Level Matching and Aggregation Network for Few-Shot Relation Classificationcited by 32
  26. 2019Hierarchical Contextualized Representation for Named Entity Recognitioncited by 32
  27. 2021Relation Classification with Entity Type Restrictioncited by 31
  28. 2020TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognitioncited by 30
  29. 2021Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemescited by 27
  30. 2021Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognitioncited by 21
  31. 2020Logic-guided Semantic Representation Learning for Zero-Shot Relation Classificationcited by 18
  32. 2018Learning Better Internal Structure of Words for Sequence Labelingcited by 8

Built on by

Not cited yet.