Visual analytics and information extraction of geological content for text-based mineral exploration reports

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dc.contributor.author Wang B.
dc.contributor.author Ma K.
dc.contributor.author Wu L.
dc.contributor.author Qiu Q.
dc.contributor.author Xie Z.
dc.contributor.author Tao L.
dc.date.accessioned 2023-07-24T05:19:12Z
dc.date.available 2023-07-24T05:19:12Z
dc.date.issued 2022
dc.identifier.citation Ore Geology Reviews, 2022, 144, 104818, p.1-12 ru_RU
dc.identifier.uri https://repository.geologyscience.ru/handle/123456789/41599
dc.description.abstract A large amount of continuously increasing textual geoscience data is stored and not fully utilized. Text mining enables the discovery and analysis of valuable information,and presents valuable insights hidden in geological texts. This research aims to use text mining and visualization techniques to obtain content words -for the purpose of visually analyzing geological reports. The framework proposed in this study can enable researchers to quickly understand key information and improve the transmission efficiency of geological reports. First, we implemented an improved keyword extraction algorithm comprising the term frequency-inverse document frequency and word length to improve the accuracy of geological keyword extraction. Second, we extracted and visualized the relative importance as well as the links between content words that can represent the key information of geoscience reports using word-level information analysis and multidimensional scaling analysis. Finally, the keyword relevance and mutual clustering relations were visualized through graphs to provide an intuitive representation of the current state of the reports. ru_RU
dc.language.iso en ru_RU
dc.subject Geoscience reports ru_RU
dc.subject Text visualization ru_RU
dc.subject Text mining ru_RU
dc.subject Natural language processing ru_RU
dc.subject Multidimensional scaling analysis ru_RU
dc.title Visual analytics and information extraction of geological content for text-based mineral exploration reports ru_RU
dc.type Article ru_RU
dc.identifier.doi 10.1016/j.oregeorev.2022.104818


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