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A Sequence Tagging based Framework for Few-Shot Relation Extraction

arXiv 2208.08053 ↗First posted 17 Aug 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

Relation Extraction (RE) refers to extracting the relation triples in the input text. Existing neural work based systems for RE rely heavily on manually labeled training data, but there are still a lot of domains where sufficient labeled data does not exist. Inspired by the distance-based few-shot named entity recognition methods, we put forward the definition of the few-shot RE task based on the sequence tagging joint extraction approaches, and propose a few-shot RE framework for the task. Besides, we apply two actual sequence tagging models to our framework (called Few-shot TPLinker and Few-shot BiTT), and achieves solid results on two few-shot RE tasks constructed from a public dataset.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 30 references that are in the map. First posted on arXiv 17 Aug 2022.

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19 of 30 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  3. 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 271
  4. 2017Meta Networkscited by 268
  5. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  6. 2016End-to-End Relation Extraction using LSTMs on Sequences and Tree Structurescited by 161
  7. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 150
  8. 2017Joint Extraction of Entities and Relations Based on a Novel Tagging Schemecited by 102
  9. 2019FewRel 2.0: Towards More Challenging Few-Shot Relation Classificationcited by 94
  10. 2020Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Networkcited by 83
  11. 2019Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extractioncited by 73
  12. 2020TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linkingcited by 72
7 more
  1. 2018Few-shot classification in Named Entity Recognition Taskcited by 64
  2. 2019CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learningcited by 47
  3. 2015Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Pathcited by 35
  4. 2021Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognitioncited by 21
  5. 2020Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learningcited by 6
  6. 2016Combining Recurrent and Convolutional Neural Networks for Relation Classificationcited by 5
  7. 2020A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extractioncited by 2

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