基于ELMO-CNN-BiLSTM-CRF模型的地质实体识别

储德平; 万波; 李红; 方芳; 王润

doi:10.3799/dqkx.2020.309

Volume 46 Issue 8

Aug. 2021

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Article Navigation > Earth Science > 2021 > 46(8): 3039-3048

Chu Deping, Wan Bo, Li Hong, Fang Fang, Wang Run, 2021. Geological Entity Recognition Based on ELMO-CNN-BiLSTM-CRF Model. Earth Science, 46(8): 3039-3048. doi: 10.3799/dqkx.2020.309

Citation:

Chu Deping, Wan Bo, Li Hong, Fang Fang, Wang Run, 2021. Geological Entity Recognition Based on ELMO-CNN-BiLSTM-CRF Model. Earth Science, 46(8): 3039-3048. doi: 10.3799/dqkx.2020.309

Chu Deping, Wan Bo, Li Hong, Fang Fang, Wang Run, 2021. Geological Entity Recognition Based on ELMO-CNN-BiLSTM-CRF Model. Earth Science, 46(8): 3039-3048. doi: 10.3799/dqkx.2020.309

Citation:

Chu Deping, Wan Bo, Li Hong, Fang Fang, Wang Run, 2021. Geological Entity Recognition Based on ELMO-CNN-BiLSTM-CRF Model. Earth Science, 46(8): 3039-3048. doi: 10.3799/dqkx.2020.309

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Geological Entity Recognition Based on ELMO-CNN-BiLSTM-CRF Model

doi: 10.3799/dqkx.2020.309

Chu Deping^{1
,
,},
Wan Bo^{1, 2
,
,
,},
Li Hong¹,
Fang Fang^{1, 2},
Wang Run^{1, 2}

1.
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
2.
National Engineering Research Center of Geographic Information System, Wuhan 430078, China

Received Date: 2020-09-17
Available Online: 2021-09-14
Publish Date: 2021-08-15

Abstract

Abstract

Geological entity is the key and core information in geological texts, and its accurate recognition is an important prerequisite for geological information extraction and mining. The ELMO-CNN-BiLSTM-CRF model is designed in this paper. Based on the pre-trained word vector, the deep BiLSTM-CRF neural network model is constructed. By adding dynamic features of words and character-level features of words, it makes up for the lack of specificity of word vectors, improves the recognition level of complex multi-word meanings in geological text and the ability to extract local features of geological entities. Taking the geological survey report of Xiongcun copper mine in Xietongmen County of Xizang Autonomous Region as an example, the performance of the model is evaluated. The accuracy rate, recall rate and F1 value of the model are 95.15%, 95.26% and 95.21% respectively. Experiments show that compared with BiLSTM-CRF and CNN-BiLSTM-CRF models, this model is more effective in small-scale corpus geological entity recognition, and can effectively identify long geological entity words and geological polysemants.
- geological big data,
- geological entity,
- named entity recognition,
- ELMO-CNN-BiLSTM-CRF,
- geological text,
- mathematical geology

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References(47)

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Geological Entity Recognition Based on ELMO-CNN-BiLSTM-CRF Model

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