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GeoTPE: A neural network model for geographical topic phrases extraction from literature based on BERT enhanced with relative position embedding

文献类型：期刊论文

作者Li, Weirong1,2,3; Sun, Kai1,2; Zhu, Yunqiang1,2,4; Ding, Fangyu1; Hu, Lei1,2,3; Dai, Xiaoliang1,2,3; Song, Jia1,2,4; Yang, Jie1,2; Qian, Lang5; Wang, Shu1,2
刊名EXPERT SYSTEMS WITH APPLICATIONS
出版日期2024
卷号235页码:14
ISSN号0957-4174
关键词GeoTPE Geographical topic phrases Geographical literature BERT Neural network Relative position embedding
DOI10.1016/j.eswa.2023.121077
通讯作者Sun, Kai(sunk@lreis.ac.cn) ; Zhu, Yunqiang(zhuyq@igsnrr.ac.cn)
英文摘要Geographical Topic Phrases (GTPs) are specialized terms for describing geographical objects, phenomena, or events and are frequently used to organize, navigate, and index geographical resources (e.g., geographical data hosted in geoportals). Typically, GTPs are stored in knowledge bases (e.g., a thesaurus). However, most existing knowledge bases are manually constructed and often updated on an annual or decennial cycle, leading to the exclusion of many newly emerging GTPs. These emerging GTPs are often discussed in geographical literature. Therefore, there is an urgent need for a method to automatically extract out-of-vocabulary GTPs from geographical literature to either create a new knowledge base or automatically update existing ones. The state-ofthe-art GTPs extraction approaches are deep learning-based models. The existing ones, however, did not consider the relative distance between vocabularies, leading to their limited capability of capturing and learning relationships between words in a sequence. In this work, we present GeoTPE, a neural network model fusing BiLSTM-CRF and BERT enhanced with relative position embedding for extracting GTPs from literature, and evaluate this model by applying it to two datasets, including a dataset harvested from geographical literature of high-ranked journals. The experimental results show that our model can not only achieve the best performance in comparison with baseline models, but also can discover novel GTPs, thus enriching existing knowledge bases.
WOS关键词KEYWORD EXTRACTION ; KNOWLEDGE ; WEB
资助项目National Key R amp; D Program of China[2021YFB3900903] ; National Key R amp; D Program of China[42050101] ; National Natural Science Foundation of China[CAS-WX2021SF-0106] ; Informatization Plan of Chinese Academy of Sciences[XDA23100100] ; Strategic Priority Research Program of the Chinese Academy of Sciences[KPI009] ; Key Project of Innovation LREIS ; [2022YFB3904201]
WOS研究方向Computer Science ; Engineering ; Operations Research & Management Science
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:001059509400001
资助机构National Key R amp; D Program of China ; National Natural Science Foundation of China ; Informatization Plan of Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Project of Innovation LREIS
源URL[http://ir.igsnrr.ac.cn/handle/311030/196849]  
专题中国科学院地理科学与资源研究所
通讯作者Sun, Kai; Zhu, Yunqiang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
5.Alibaba Grp, Hangzhou 310000, Peoples R China
推荐引用方式
GB/T 7714		 Li, Weirong,Sun, Kai,Zhu, Yunqiang,et al. GeoTPE: A neural network model for geographical topic phrases extraction from literature based on BERT enhanced with relative position embedding[J]. EXPERT SYSTEMS WITH APPLICATIONS,2024,235:14.
APA Li, Weirong.,Sun, Kai.,Zhu, Yunqiang.,Ding, Fangyu.,Hu, Lei.,...&Wang, Shu.(2024).GeoTPE: A neural network model for geographical topic phrases extraction from literature based on BERT enhanced with relative position embedding.EXPERT SYSTEMS WITH APPLICATIONS,235,14.
MLA Li, Weirong,et al."GeoTPE: A neural network model for geographical topic phrases extraction from literature based on BERT enhanced with relative position embedding".EXPERT SYSTEMS WITH APPLICATIONS 235(2024):14.

入库方式： OAI收割

来源：地理科学与资源研究所

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