A Two-Stage Bi-LSTM Model for Chinese Company Name Recognition

Zeng, Jing; Che, Jin; Xing, Chunxiao; Zhang, Liang-Jie

doi:10.1007/978-3-319-94361-9_1

Cited by 3 publications

(1 citation statement)

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“…The researchemerged. Research and experiments with the LSTM (Y. Wang et al, ; Zeng et al, ) on Chinese named entity recognition have been carried out.…”

Section: Related Researchmentioning

confidence: 99%

Bi‐LSTM+CRF‐based named entity recognition in scientific papers in the field of ecological restoration technology

Yuan

2019

Proceedings of the Association for Information Science and Tech

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We carried out an experiment to extract ecological‐restoration‐technology‐related entities (i.e., entities related to the fragile ecological remediation technology of desertification, rocky desertification, water and soil erosion control areas, place entities, and time entities related to the technology performed,) from full‐text documents published in the CNKI from 1978 to 2017. Based on the extraction of the time entities, place entities, and fragile ecological remediation technology entities, we analyzed the technologies with the greatest potential from among China's fragile ecological restoration technologies using the category clustering of fragile ecological restoration technologies with LDA and the place distribution and evolution of fragile ecological treatment technologies. This research shows that the proposed Bi‐LSTM+CRF combined with the feature‐based named entity knowledgebase can be used to perform geographical knowledge discovery from text, and it has has good prospects for applications in intelligence analysis based on text mining.

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“…The researchemerged. Research and experiments with the LSTM (Y. Wang et al, ; Zeng et al, ) on Chinese named entity recognition have been carried out.…”

Section: Related Researchmentioning

confidence: 99%