دورية أكاديمية
Spatio-Temporal Information Extraction and Geoparsing for Public Chinese Resumes
| العنوان: | Spatio-Temporal Information Extraction and Geoparsing for Public Chinese Resumes |
|---|---|
| المؤلفون: | Xiaolong Li, Wu Zhang, Yanjie Wang, Yongbin Tan, Jing Xia |
| المصدر: | ISPRS International Journal of Geo-Information, Vol 12, Iss 9, p 377 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Geography (General) |
| مصطلحات موضوعية: | named entity recognition (NER), resume information extraction, geoparsing, natural language processing (NLP), deep learning, Geography (General), G1-922 |
| الوصف: | As an important carrier of individual information, the resume is an important data source for studying the spatio-temporal evolutionary characteristics of individual and group behaviors. This study focuses on spatio-temporal information extraction and geoparsing from resumes to provide basic technical support for spatio-temporal research based on resume text. Most current studies on resume text information extraction are oriented toward recruitment work, such as the automated information extraction, classification, and recommendation of resumes. These studies ignore the spatio-temporal information of individual and group behaviors implied in resumes. Therefore, this study takes the public resumes of teachers in key universities in China as the research data, proposes a set of spatio-temporal information extraction solutions for electronic resumes of public figures, and designs a spatial entity geoparsing method, which can effectively extract and spatially locate spatio-temporal information in the resumes. To verify the effectiveness of the proposed method, text information extraction models such as BiLSTM-CRF, BERT-CRF, and BERT-BiLSTM-CRF are selected to conduct comparative experiments, and the spatial entity geoparsing method is verified. The experimental results show that the precision of the selected models on the named entity recognition task is 96.23% and the precision of the designed spatial entity geoparsing method is 97.91%. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2220-9964 73463019 |
| Relation: | https://www.mdpi.com/2220-9964/12/9/377; https://doaj.org/toc/2220-9964 |
| DOI: | 10.3390/ijgi12090377 |
| URL الوصول: | https://doaj.org/article/70f7346301954f259c1b5b2c378e7655 |
| رقم الأكسشن: | edsdoj.70f7346301954f259c1b5b2c378e7655 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 22209964 73463019 |
|---|---|
| DOI: | 10.3390/ijgi12090377 |