DePNR: A DeBERTa-based deep learning model with complete position embedding for place name recognition from geographical literature
文献类型：期刊论文
| 作者 | Li, Weirong5,6; Sun, Kai4; Wang, Shu3; Zhu, Yunqiang2,3; Dai, Xiaoliang1,3; Hu, Lei1,3 |
| 刊名 | TRANSACTIONS IN GIS
 |
| 出版日期 | 2024-05-03 |
| 卷号 | N/A |
| DOI | 10.1111/tgis.13170 |
| 产权排序 | 4 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Place names play an important role in linking physical places to human perception and are highly frequently used in the daily lives of people to refer to places in natural language. However, many place names may not be recorded in typical gazetteers due to their new establishment, colloquial nature, and different concerns. These unrecorded toponyms are often discussed in geographical literature; thus, it is necessary to automatically identify them from geographical literature and update existing gazetteers using computational approaches. Currently, the most advanced approaches are deep learning-based models. However, existing models used only partial position information rather than complete position information of words in a sentence, which limits their performance in recognizing toponyms. To this end, we develop DePNR, a DeBERTa-based deep learning model with complete position embedding for place name recognition from geographical literature. We train DePNR on two datasets and test it on a real dataset from geographical literature to evaluate its performance. The results show that DePNR achieves an F-score of 0.8282, outperforming previous approaches, and can recognize new toponyms from literature text, potentially enriching existing gazetteers. |
| WOS研究方向 | Geography |
| WOS记录号 | WOS:001217097300001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205165]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhu, Yunqiang |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Univ Buffalo, Dept Geog, GeoAI Lab, Buffalo, NY 14068 USA 5.Guangxi Normal Univ, Guangxi Key Lab Environm Proc & Remediat Ecol Frag, Guilin, Peoples R China 6.Guangxi Normal Univ, Coll Environm & Resources, Guilin, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Weirong,Sun, Kai,Wang, Shu,et al. DePNR: A DeBERTa-based deep learning model with complete position embedding for place name recognition from geographical literature[J]. TRANSACTIONS IN GIS,2024,N/A. |
| APA | Li, Weirong,Sun, Kai,Wang, Shu,Zhu, Yunqiang,Dai, Xiaoliang,&Hu, Lei.(2024).DePNR: A DeBERTa-based deep learning model with complete position embedding for place name recognition from geographical literature.TRANSACTIONS IN GIS,N/A. |
| MLA | Li, Weirong,et al."DePNR: A DeBERTa-based deep learning model with complete position embedding for place name recognition from geographical literature".TRANSACTIONS IN GIS N/A(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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