BiLSTM-CRF for geological named entity recognition from the geoscience literature-Reference-Cited by-同舟云学术

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BiLSTM-CRF for geological named entity recognition from the geoscience literature

Published:2019-08-16 Issue:4 Volume:12 Page:565-579
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Qiu Qinjun,Xie Zhong,Wu Liang,Tao Liufeng,Li Wenjia

Funder

National Natural Science Foundation of China

the National Natural Science Foundation of China

the National Key Research and Development Program

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

http://link.springer.com/content/pdf/10.1007/s12145-019-00390-3.pdf

Reference53 articles.

1. Arnab A, Jayasumana S, Zheng S, & Torr, PH (2016) Higher order conditional random fields in deep neural networks. European Conference on Computer Vision 524–540

2. Babaie HA, Davarpanah A (2018) Semantic modeling of plastic deformation of polycrystalline rock. Comput Geosci 111:213–222

3. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166

4. Cernuzzi L, Pane J (2014) Toward open government in Paraguay[J]. It Professional 16(5):62–64

5. Chen X, Shi Z, Qiu X, et al (2017) Adversarial multi-criteria learning for Chinese word segmentation. arXiv, arXiv:1193–1203

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