Global Span Selection for Named Entity Recognition
Urchade Zaratiana, Niama El Elkhbir, Pierre Holat, Nadi Tomeh, Thierry Charnois
Abstract
Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.- Anthology ID:
- 2022.umios-1.2
- Volume:
- Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Wenjuan Han, Zilong Zheng, Zhouhan Lin, Lifeng Jin, Yikang Shen, Yoon Kim, Kewei Tu
- Venue:
- UM-IoS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11–17
- Language:
- URL:
- https://aclanthology.org/2022.umios-1.2
- DOI:
- 10.18653/v1/2022.umios-1.2
- Cite (ACL):
- Urchade Zaratiana, Niama El Elkhbir, Pierre Holat, Nadi Tomeh, and Thierry Charnois. 2022. Global Span Selection for Named Entity Recognition. In Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS), pages 11–17, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Global Span Selection for Named Entity Recognition (Zaratiana et al., UM-IoS 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.umios-1.2.pdf