Abstract
Spatial information extraction is essential to understand geographical information in text. This task is largely divided to two subtasks: spatial element extraction and spatial relation extraction. In this paper, we utilize BERT (Devlin et al., 2018), which is very effective for many natural language processing applications. We propose a BERT-based spatial information extraction model, which uses BERT for spatial element extraction and R-BERT (Wu and He, 2019) for spatial relation extraction. The model was evaluated with the SemEval 2015 dataset. The result showed a 15.4% point increase in spatial element extraction and an 8.2% point increase in spatial relation extraction in comparison to the baseline model (Nichols and Botros, 2015).- Anthology ID:
- 2020.splu-1.2
- Volume:
- Proceedings of the Third International Workshop on Spatial Language Understanding
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- SpLU
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10–17
- Language:
- URL:
- https://aclanthology.org/2020.splu-1.2
- DOI:
- 10.18653/v1/2020.splu-1.2
- Cite (ACL):
- Hyeong Jin Shin, Jeong Yeon Park, Dae Bum Yuk, and Jae Sung Lee. 2020. BERT-based Spatial Information Extraction. In Proceedings of the Third International Workshop on Spatial Language Understanding, pages 10–17, Online. Association for Computational Linguistics.
- Cite (Informal):
- BERT-based Spatial Information Extraction (Shin et al., SpLU 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.splu-1.2.pdf