Example-Based Named Entity Recognition

We present a novel approach to named entity recognition (NER) in the presence of scarce data that we call example-based NER. Our train-free few-shot learning approach takes inspiration from question-answering to identify entity spans in a new and unseen domain. In comparison with the current state-of-the-art, the proposed method performs significantly better, especially when using a low number of support examples.

[1]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[2]  Jiwei Li,et al.  A Unified MRC Framework for Named Entity Recognition , 2019, ACL.

[3]  Yaojie Lu,et al.  A Rigourous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land? , 2020, EMNLP.

[4]  Iryna Gurevych,et al.  Low Resource Sequence Tagging with Weak Labels , 2020, AAAI.

[5]  Ani Nenkova,et al.  Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve , 2020, ArXiv.

[6]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[7]  Philip Yu,et al.  MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing , 2020, COLING.

[8]  Huajun Chen,et al.  Improving Few-shot Text Classification via Pretrained Language Representations , 2019, ArXiv.

[9]  Bing Li,et al.  Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations , 2020, AAAI.

[10]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[11]  Pierre Lison,et al.  Named Entity Recognition without Labelled Data: A Weak Supervision Approach , 2020, ACL.

[12]  Fei Wang,et al.  Coreference Resolution as Query-based Span Prediction , 2019, ArXiv.

[13]  Xin Li,et al.  A Chinese Corpus for Fine-grained Entity Typing , 2020, LREC.

[14]  Ming-Wei Chang,et al.  REALM: Retrieval-Augmented Language Model Pre-Training , 2020, ICML.

[15]  Varvara Logacheva,et al.  Few-shot classification in named entity recognition task , 2018, SAC.

[16]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[17]  Chao Zhang,et al.  Partially-Typed NER Datasets Integration: Connecting Practice to Theory , 2020, ArXiv.

[18]  Richard S. Zemel,et al.  Prototypical Networks for Few-shot Learning , 2017, NIPS.

[19]  Ani Nenkova,et al.  Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models , 2020, ArXiv.

[20]  Karl Stratos,et al.  Label-Agnostic Sequence Labeling by Copying Nearest Neighbors , 2019, ACL.

[21]  Erik F. Tjong Kim Sang,et al.  Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition , 2003, CoNLL.

[22]  Colin Raffel,et al.  Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..

[23]  Percy Liang,et al.  Know What You Don’t Know: Unanswerable Questions for SQuAD , 2018, ACL.

[24]  Mark Chen,et al.  Language Models are Few-Shot Learners , 2020, NeurIPS.

[25]  Wei Qiu,et al.  Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition , 2020, AAAI.