Abstract
Active Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance. Previous works have shown that lightweight architectures for Named Entity Recognition (NER) can achieve optimal performance with only 25% of the original training data. However, these methods do not exploit the sequential nature of language and the heterogeneity of uncertainty within each instance, requiring the labelling of whole sentences. Additionally, this standard method requires that the annotator has access to the full sentence when labelling. In this work, we overcome these limitations by allowing the AL algorithm to query subsequences within sentences, and propagate their labels to other sentences. We achieve highly efficient results on OntoNotes 5.0, only requiring 13% of the original training data, and CoNLL 2003, requiring only 27%. This is an improvement of 39% and 37% compared to querying full sentences.- Anthology ID:
- 2021.acl-long.332
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4310–4321
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.332
- DOI:
- 10.18653/v1/2021.acl-long.332
- Cite (ACL):
- Puria Radmard, Yassir Fathullah, and Aldo Lipani. 2021. Subsequence Based Deep Active Learning for Named Entity Recognition. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4310–4321, Online. Association for Computational Linguistics.
- Cite (Informal):
- Subsequence Based Deep Active Learning for Named Entity Recognition (Radmard et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.acl-long.332.pdf
- Data
- CoNLL 2003