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5. GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts

Author

Wang, Dongbo, Liu, Chang, Zhao, Zhixiao, Shen, Si, Liu, Liu, Li, Bin, Hu, Haotian, Wu, Mengcheng, Lin, Litao, Zhao, Xue, and Wang, Xiyu

Subjects
Computer Science - Computation and Language
Abstract

In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era., Comment: 22pages,0 figure

Published
2023

8. Evaluation of Ancient Chinese Natural Language Understanding in Large Language Models Based on ACHNLU

Author

Hu, Die, Sun, Guangyao, Liu, Liu, Liu, Chang, Wang, Dongbo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sserwanga, Isaac, editor, Joho, Hideo, editor, Ma, Jie, editor, Hansen, Preben, editor, Wu, Dan, editor, Koizumi, Masanori, editor, and Gilliland, Anne J., editor

Published
2024
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9. AIGC-Enabled Interdisciplinary Science Measurement

Author

Liu, Jiangfeng, Wang, Xiyu, Wang, Dongbo, Pei, Lei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sserwanga, Isaac, editor, Joho, Hideo, editor, Ma, Jie, editor, Hansen, Preben, editor, Wu, Dan, editor, Koizumi, Masanori, editor, and Gilliland, Anne J., editor

Published
2024
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13. SsciBERT: A Pre-trained Language Model for Social Science Texts

Author

Shen, Si, Liu, Jiangfeng, Lin, Litao, Huang, Ying, Zhang, Lin, Liu, Chang, Feng, Yutong, and Wang, Dongbo

Subjects
Computer Science - Computation and Language
Abstract

The academic literature of social sciences records human civilization and studies human social problems. With its large-scale growth, the ways to quickly find existing research on relevant issues have become an urgent demand for researchers. Previous studies, such as SciBERT, have shown that pre-training using domain-specific texts can improve the performance of natural language processing tasks. However, the pre-trained language model for social sciences is not available so far. In light of this, the present research proposes a pre-trained model based on the abstracts published in the Social Science Citation Index (SSCI) journals. The models, which are available on GitHub (https://github.com/S-T-Full-Text-Knowledge-Mining/SSCI-BERT), show excellent performance on discipline classification, abstract structure-function recognition, and named entity recognition tasks with the social sciences literature., Comment: 24 pages,2 figures

Published
2022

47. A study on variable operating characteristics of a direct air-cooling exhaust steam extraction heat supply system

Author

XING Yue, TAN Haoyi, PEI Dongsheng, WANG Dongbo, and FAN Chenghao

Subjects
exhaust steam extraction heat supply, direct air-cooling, variable operating characteristic, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The high backpressure heating mode of direct air-cooling unit faces limitations due to the return water temperature of heat network and the coordinated heat exchanger unit control of air-cooling island. This limitation complicates the task of achieving a balance between operation economy and power generation output. To solve this inherent contradiction， Ebsilon Professional， a commercial software， is used to calculate and analyze the key parameters of the direct air-cooling exhaust steam extraction heat supply system. These parameters include the backpressure of the air-cooling island， steam turbine output and heat consumption， and suction coefficient of the injector under different loads and variable operating conditions. The calculations reveal that， during low load in winter， the optimal operating backpressure for the heat supply system falls within the range of 13~20 kPa， which allows for the gradient utilization of heating energy， ensures a balance in unit output， and meets the safety requirements for preventing freezing.

Published
2023
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