GeoDocA – Fast analysis of geological content in mineral exploration reports: A text mining approach

Holden, Eun‐Jung; Liu, Wei; Horrocks, Tom; Wang, Rui; Wedge, Daniel; Duuring, Paul; Beardsmore, T.

doi:10.1016/j.oregeorev.2019.05.005

Cited by 43 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the domain of geosciences, various systems and applications, such as mineral exploration (Holden et al, 2019;Shi et al, 2018), paleontological studies (Peters et al, 2014(Peters et al, , 2017Wang et al, 2018), and geological text mining and application in Chinese (Qiu, Xie, Wu, Tao, & Li, 2019;Wang et al, 2018), have been developed and constructed. Holden et al (2019) analyzed 25,419 mineral exploration reports in a targeted manner using NLP pipeline analysis methods (word recognition, keyword extraction, etc. ), focusing on searching and summarizing the geology-related content in the reports, and developed a related system named the GeoDocA system.…”

Section: Text Mining In Geosciencementioning

confidence: 99%

“…In the domain of geosciences, various systems and applications, such as mineral exploration (Holden et al., 2019; Shi et al., 2018), paleontological studies (Peters et al., 2014, 2017; Wang et al., 2018), and geological text mining and application in Chinese (Qiu, Xie, Wu, Tao, & Li, 2019; Qiu, Xie, Wu, & Tao, 2019; Wang et al., 2018), have been developed and constructed.…”

Section: Related Workmentioning

confidence: 99%

“…Holden et al. (2019) analyzed 25,419 mineral exploration reports in a targeted manner using NLP pipeline analysis methods (word recognition, keyword extraction, etc. ), focusing on searching and summarizing the geology‐related content in the reports, and developed a related system named the GeoDocA system.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

Xie

Xu³

et al. 2022

Earth and Space Science

View full text Add to dashboard Cite

Geological reports are frequently used by geologists involved in geological surveys and scientific research to record the results and outcomes of geological surveys. With such a rich data source, a substantial amount of knowledge has yet to be mined and analyzed. This paper focuses on automatically information extraction from geological reports, namely, geological named entity recognition. Geological named entity recognition has an important role in data mining, knowledge discovery and Knowledge graph construction. Existing general named entity recognition models/tools are limited in the domain of geoscience due to the various language irregularities associated with geological text, such as informal sentence structures, several domain‐geoscience words, large character lengths and multiple combinations of independent words. We present Bidirectional encoder representations from transformers (BERT)‐(Bidirectional gated recurrent unit network) BiGRU‐ (Conditional random field) CRF, which is a deep learning‐based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. Based on the pretrained language model, an integrated deep learning model incorporating BERT, BiGRU and CRF is constructed to obtain character vectors rich in semantic information through the BERT pretrained language model to alleviate for the lack of specificity of static word vectors (e.g., word2vec) and to improve the extraction capability of complex geological entities. We demonstrate our proposed model by applying it to four test datasets, including a geoscience NER data set from regional geological reports, and by comparing its performance with those of five baseline models.

show abstract

Section: Text Mining In Geosciencementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

Xie

Xu³

et al. 2022

Earth and Space Science

View full text Add to dashboard Cite

show abstract

“…The authors compared their methodology with a set of state-of-the-art approaches and achieved average F 1 -scores of 30.1% and 40.7% on two constructed data sets. Holden et al (2019) developed a geological report/document analysis model/system (namely, GeoDocA) to browse and search for documents/reports by relevant geological contents. GeoDocA applied a set of keyphrases to broad classes including mineral occurrences information, rock structure, alteration types, and geological time.…”

Section: For Geological Data Miningmentioning

confidence: 99%

Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm

Qiu

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

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.

show abstract

“…This abundance of unstructured data stands out due to its extensive geological content, encompassing substantial amounts of geological information and knowledge [8][9][10][11][12]. At present, unstructured data are stored in different file formats, such as Word, Excel, tables, PDF, etc., and are managed by file-based systems or relational databases [13][14][15]. However, due to the heterogeneous and fragmented nature of unstructured data, using traditional file systems or relational databases to manage the data will lead to inefficient data querying, counting, and updating operations, making it difficult to make effective use of the explicit content information and even more difficult to explore the richer knowledge information implicitly contained in the data [16,17].…”

Section: Introductionmentioning

confidence: 99%

Developing a Base Domain Ontology from Geoscience Report Collection to Aid in Information Retrieval towards Spatiotemporal and Topic Association

Tao,

Ma,

Tian

et al. 2023

IJGI

View full text Add to dashboard Cite

The efficient and precise retrieval of desired information from extensive geological databases is a prominent and pivotal focus within the realm of geological information services. Conventional information retrieval methods primarily rely on keyword matching approaches, which often overlook the contextual and semantic aspects of the keywords, consequently impeding the retrieval system’s ability to accurately comprehend user query requirements. To tackle this challenge, this study proposes an ontology-driven information-retrieval framework for geological data that integrates spatiotemporal and topic associations. The framework encompasses the development of a geological domain ontology, extraction of key information, establishment of a multi-feature association and retrieval framework, and validation through a comprehensive case study. By employing the proposed framework, users are empowered to actively and automatically retrieve pertinent information, simplifying the information access process, mitigating the burden of comprehending information organization and software application models, and ultimately enhancing retrieval efficiency.

show abstract

GeoDocA – Fast analysis of geological content in mineral exploration reports: A text mining approach

Cited by 43 publications

References 13 publications

Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm

Developing a Base Domain Ontology from Geoscience Report Collection to Aid in Information Retrieval towards Spatiotemporal and Topic Association

Contact Info

Product

Resources

About