Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning

Yi Yang, Arzoo Katiyar


Abstract
We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference. Our system uses a supervised NER model trained on the source domain, as a feature extractor. Across several test domains, we show that a nearest neighbor classifier in this feature-space is far more effective than the standard meta-learning approaches. We further propose a cheap but effective method to capture the label dependencies between entity tags without expensive CRF training. We show that our method of combining structured decoding with nearest neighbor learning achieves state-of-the-art performance on standard few-shot NER evaluation tasks, improving F1 scores by 6% to 16% absolute points over prior meta-learning based systems.
Anthology ID:
2020.emnlp-main.516
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6365–6375
Language:
URL:
https://aclanthology.org/2020.emnlp-main.516
DOI:
10.18653/v1/2020.emnlp-main.516
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.516.pdf
Video:
 https://slideslive.com/38939200