Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm
Abstract Massive unstructured geoscience data are buried in geological reports. Geological text classification provides opportunities to leverage this wealth of data for geology and mineralization research. Existing studies of massive geoscience documents/reports have not provided effective classifi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2020-03-01
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| Series: | Earth and Space Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2019EA000993 |
| _version_ | 1811312830890115072 |
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| author | Qinjun Qiu Zhong Xie Liang Wu Liufeng Tao |
| author_facet | Qinjun Qiu Zhong Xie Liang Wu Liufeng Tao |
| author_sort | Qinjun Qiu |
| collection | DOAJ |
| description | Abstract Massive unstructured geoscience data are buried in geological reports. Geological text classification provides opportunities to leverage this wealth of data for geology and mineralization research. Existing studies of massive geoscience documents/reports have not provided effective classification results for further knowledge discovery and data mining and often lack adequate domain‐specific knowledge. In this paper, we present a novel and unified framework (namely, Dic‐Att‐BiLSTM) that combines domain‐specific knowledge and bidirectional long short‐term memory (BiLSTM) for effective geological text classification. Dic‐Att‐BiLSTM benefits from a matching strategy by incorporating domain‐specific knowledge developed based on geoscience ontology to grasp the linguistic geoscience clues. Furthermore, Dic‐Att‐BiLSTM brings together the capacity of a geoscience dictionary matching approach and an attention mechanism to construct a dictionary attention layer. Finally, the network framework of Dic‐Att‐BiLSTM can utilize domain‐specific knowledge and classify geological text automatically. Experimental verifications are conducted on two constructed data sets, and the results clearly indicate that Dic‐Att‐BiLSTM outperforms other state‐of‐the‐art text classification models. |
| first_indexed | 2024-04-13T10:43:30Z |
| format | Article |
| id | doaj.art-435ba9ad04cd489faa659ae7e109e468 |
| institution | Directory Open Access Journal |
| issn | 2333-5084 |
| language | English |
| last_indexed | 2024-04-13T10:43:30Z |
| publishDate | 2020-03-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj.art-435ba9ad04cd489faa659ae7e109e4682022-12-22T02:49:52ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842020-03-0173n/an/a10.1029/2019EA000993Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning AlgorithmQinjun Qiu0Zhong Xie1Liang Wu2Liufeng Tao3School of Geography and Information Engineering China University of Geosciences Wuhan ChinaSchool of Geography and Information Engineering China University of Geosciences Wuhan ChinaSchool of Geography and Information Engineering China University of Geosciences Wuhan ChinaSchool of Geography and Information Engineering China University of Geosciences Wuhan ChinaAbstract Massive unstructured geoscience data are buried in geological reports. Geological text classification provides opportunities to leverage this wealth of data for geology and mineralization research. Existing studies of massive geoscience documents/reports have not provided effective classification results for further knowledge discovery and data mining and often lack adequate domain‐specific knowledge. In this paper, we present a novel and unified framework (namely, Dic‐Att‐BiLSTM) that combines domain‐specific knowledge and bidirectional long short‐term memory (BiLSTM) for effective geological text classification. Dic‐Att‐BiLSTM benefits from a matching strategy by incorporating domain‐specific knowledge developed based on geoscience ontology to grasp the linguistic geoscience clues. Furthermore, Dic‐Att‐BiLSTM brings together the capacity of a geoscience dictionary matching approach and an attention mechanism to construct a dictionary attention layer. Finally, the network framework of Dic‐Att‐BiLSTM can utilize domain‐specific knowledge and classify geological text automatically. Experimental verifications are conducted on two constructed data sets, and the results clearly indicate that Dic‐Att‐BiLSTM outperforms other state‐of‐the‐art text classification models.https://doi.org/10.1029/2019EA000993Long short‐term memoryGeoscience ontologyAttention mechanismText classification |
| spellingShingle | Qinjun Qiu Zhong Xie Liang Wu Liufeng Tao Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm Earth and Space Science Long short‐term memory Geoscience ontology Attention mechanism Text classification |
| title | Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm |
| title_full | Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm |
| title_fullStr | Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm |
| title_full_unstemmed | Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm |
| title_short | Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm |
| title_sort | dictionary based automated information extraction from geological documents using a deep learning algorithm |
| topic | Long short‐term memory Geoscience ontology Attention mechanism Text classification |
| url | https://doi.org/10.1029/2019EA000993 |
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