Satellite and instrument entity recognition using a pre-trained language model with distant supervision

Lin, Ming‐Cheng; Jin, Meng; Liu, Yufu; Bai, Yuqi

doi:10.1080/17538947.2022.2107098

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…In terms of the node recognition, the method utilized in this section comprehensively integrates multiple recognition dimensions, such as the word frequency, the part of speech, and the length of the word, which divides the names of China’s government departments and public organizations into three parts, i.e., the prefix word (P), the middle word (M), and the tagged word (T), where the tagged word (T) is the core part in the recognition process. In addition, because many pronouns and abbreviations can be used to denote these government departments and public organizations in the common expressions, this paper further optimizes the original dictionary used in the named entity recognition process by collecting and adding theses corresponding pronouns and abbreviations [ 24 , 25 , 26 ]. The node recognition processes of the NIS are shown in Figure 2 .…”

Section: The Healthcare–nursing Information Collaboration Network (Hn...mentioning

confidence: 99%

Promoting the Integration of Elderly Healthcare and Elderly Nursing: Evidence from the Chinese Government

Hao

Yin

2022

IJERPH

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The increase of the aging population in China and the rise of the concept of healthy aging have accelerated the transformation and upgrading of the traditional elderly nursing pattern. Nevertheless, there is a critical limitation existing in the current situation of China’s elderly care, i.e., the medical institutions do not support elderly nursing and the elderly nursing institutions do not facilitate access to medical care. To eliminate the adverse impact of this issue, twelve ministries and commissions of the Chinese government have jointly issued a document, i.e., the Several Opinions on Further Promoting the Development of Combining the Healthcare with the Elderly care (SOFPDCHE), to provide guidance from the government level for further promoting the integration of elderly healthcare and elderly nursing. Under this background, this paper constructs a healthcare–nursing information collaboration network (HnICN) based on the SOFPDCHE, proposing three novel strategies to explore the different roles and collaboration relationships of relevant government departments and public organizations in this integration process, i.e., the node identification strategy (NIS), the local adjacency subgroup strategy (LASS), and the information collaboration effect measurement strategy (ICEMS). Furthermore, this paper retrieves 484 valid policy documents related to “the integration of elderly healthcare and elderly nursing” as data samples on the official websites of 12 sponsored ministries and commissions, and finally confirms 22 government departments and public organizations as the network nodes based on these obtained documents, such as the National Health Commission of the People’s Republic of China (NHC), the Ministry of Industry and Information Technology of the People’s Republic of China (MIIT), and the National Working Commission on Aging (NWCA). In terms of the collaboration effect, the results of all node-pairs in the HnICN are significantly different, where the collaboration effect between the NHC and MIIT is best and that between the NATCM and MIIT is second best, which are 84.572% and 20.275%, respectively. This study provides the quantifiable results of the information collaboration degree between different government agencies and forms the optimization scheme for the current collaboration status based on these results, which play a positive role in integrating elderly healthcare and elderly nursing and eventually achieving healthy aging.

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Section: The Healthcare–nursing Information Collaboration Network (Hn...mentioning

confidence: 99%

Promoting the Integration of Elderly Healthcare and Elderly Nursing: Evidence from the Chinese Government

Hao

Yin

2022

IJERPH

View full text Add to dashboard Cite

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Managing Comprehensive Research Instrument Descriptions Within a Scholarly Knowledge Graph

Haris,

Auer,

Stocker

2024

Lecture Notes in Computer Science

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PreparedLLM: effective pre -pretraining framework for domain-specific large language models

Chen,

Lin,

Wang

et al. 2024

Big Earth Data

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Satellite and instrument entity recognition using a pre-trained language model with distant supervision

Cited by 4 publications

References 26 publications

Promoting the Integration of Elderly Healthcare and Elderly Nursing: Evidence from the Chinese Government

Promoting the Integration of Elderly Healthcare and Elderly Nursing: Evidence from the Chinese Government

Managing Comprehensive Research Instrument Descriptions Within a Scholarly Knowledge Graph

PreparedLLM: effective pre -pretraining framework for domain-specific large language models

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