Abstract
In this paper, we observe and address the challenges of the coordination recognition task. Most existing methods rely on syntactic parsers to identify the coordinators in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. To better solve the problems, we propose a pipeline model COordination RECognizer (CoRec). It consists of two components: coordinator identifier and conjunct boundary detector. The experimental results on datasets from various domains demonstrate the effectiveness and efficiency of the proposed method. Further experiments show that CoRec positively impacts downstream tasks, improving the yield of state-of-the-art Open IE models.- Anthology ID:
- 2023.emnlp-main.934
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15112–15120
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.934
- DOI:
- 10.18653/v1/2023.emnlp-main.934
- Cite (ACL):
- Qing Wang, Haojie Jia, Wenfei Song, and Qi Li. 2023. CoRec: An Easy Approach for Coordination Recognition. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15112–15120, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- CoRec: An Easy Approach for Coordination Recognition (Wang et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2023.emnlp-main.934.pdf