@inproceedings{shin-etal-2020-bert,
title = "{BERT}-based Spatial Information Extraction",
author = "Shin, Hyeong Jin and
Park, Jeong Yeon and
Yuk, Dae Bum and
Lee, Jae Sung",
editor = "Kordjamshidi, Parisa and
Bhatia, Archna and
Alikhani, Malihe and
Baldridge, Jason and
Bansal, Mohit and
Moens, Marie-Francine",
booktitle = "Proceedings of the Third International Workshop on Spatial Language Understanding",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.splu-1.2",
doi = "10.18653/v1/2020.splu-1.2",
pages = "10--17",
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).",
}
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<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).</abstract>
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%0 Conference Proceedings
%T BERT-based Spatial Information Extraction
%A Shin, Hyeong Jin
%A Park, Jeong Yeon
%A Yuk, Dae Bum
%A Lee, Jae Sung
%Y Kordjamshidi, Parisa
%Y Bhatia, Archna
%Y Alikhani, Malihe
%Y Baldridge, Jason
%Y Bansal, Mohit
%Y Moens, Marie-Francine
%S Proceedings of the Third International Workshop on Spatial Language Understanding
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F shin-etal-2020-bert
%X 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).
%R 10.18653/v1/2020.splu-1.2
%U https://aclanthology.org/2020.splu-1.2
%U https://doi.org/10.18653/v1/2020.splu-1.2
%P 10-17
Markdown (Informal)
[BERT-based Spatial Information Extraction](https://aclanthology.org/2020.splu-1.2) (Shin et al., SpLU 2020)
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.