# A Sequence Tagging based Framework for Few-Shot Relation Extraction

arXiv: https://arxiv.org/abs/2208.08053 · HTML: https://mlsanity.com/paper/2208.08053/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Meta Networks](https://mlsanity.com/paper/1703.00837/) (2017)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [FewRel 2.0: Towards More Challenging Few-Shot Relation Classification](https://mlsanity.com/paper/1910.07124/) (2019)
- [Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network](https://mlsanity.com/paper/2006.05702/) (2020)
- [Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction](https://mlsanity.com/paper/1911.09886/) (2019)
- [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://mlsanity.com/paper/2010.13415/) (2020)
- [Few-shot classification in Named Entity Recognition Task](https://mlsanity.com/paper/1812.06158/) (2018)
- [CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning](https://mlsanity.com/paper/1911.10438/) (2019)
- [Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path](https://mlsanity.com/paper/1508.03720/) (2015)
- [Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2106.15167/) (2021)
- [Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning](https://mlsanity.com/paper/2010.02405/) (2020)
- [Combining Recurrent and Convolutional Neural Networks for Relation Classification](https://mlsanity.com/paper/1605.07333/) (2016)
- [A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction](https://mlsanity.com/paper/2008.13339/) (2020)

## 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.