知识组织

跨学科知识扩散视域下学科交叉科学术语识别与特征计算

  • 孔玲 ,
  • 胡昊天 ,
  • 张卫 ,
  • 王东波 ,
  • 叶文豪 ,
  • 白如江 ,
  • 王效岳
展开
  • 1 山东理工大学信息管理学院 淄博 255049;
    2 南京大学信息管理学院 南京 210023;
    3 南京农业大学信息管理学院 南京 210095
孔玲,讲师,博士;胡昊天,博士研究生;张卫,青年研究员,博士;王东波,教授,博士,博士生导师,通信作者, E-mail:db.wang@njau.edu.cn;叶文豪,青年研究员,博士;白如江,教授,博士,博士生导师;王效岳,教授,博士,硕士生导师。

收稿日期: 2023-10-30

  修回日期: 2024-01-26

  网络出版日期: 2024-06-29

基金资助

本文系国家自然科学基金青年项目“面向科技项目评价的创新知识图谱构建及知识推理研究”(项目编号: 7230414 2)和国家社会科学基金项目“基于文本内容挖掘的学术论文影响力评价研究”(项目编号: 19BTQ085)研究成果之一。

Identification and Feature Computation of Interdisciplinary Scientific Terms in the Perspective of Transdisciplinary Knowledge Diffusion

  • Kong Ling ,
  • Hu Haotian ,
  • Zhang Wei ,
  • Wang Dongbo ,
  • Ye Wenhao ,
  • Bai Rujiang ,
  • Wang Xiaoyue
Expand
  • 1 School of Information Management, Shandong University of Technology, Zibo 255049;
    2 School of Information Management, Nanjing University, Nanjing 210023;
    3 School of Information Management, Nanjing Agricultural University, Nanjing 210095
Kong Ling, lecturer, PhD; Hu Haotian, doctoral candidate; Zhang Wei, young researcher, PhD; Wang Dongbo, professor, PhD, doctoral supervisor, corresponding author, E-mail: db.wang@njau.edu.cn; Ye Wenhao, young researcher, PhD; Bai Rujiang, professor, PhD, doctoral supervisor; Wang Xiaoyue, professor, PhD, master supervisor.

Received date: 2023-10-30

  Revised date: 2024-01-26

  Online published: 2024-06-29

Supported by

This work is supported by the youth project of National Natural Science Foundation of China titled “Research on Innovation Knowledge Mapping and Knowledge Reasoning for Evaluation of Scientific and Technological Projects” (Grant No. 72304142) and the project of National Social Science Fund of China’s titled “Research on Evaluation of the Impact of Academic Papers Based on Text Content Mining” (Grant No. 19BTQ085).

摘要

[ 目的 / 意义 ] 科学术语承载学科基础知识与核心概念,对跨学科知识扩散的学术全文本引文中科学术语进行知识挖掘与特征计算,对深入探究跨学科的知识体系交叉融合规律与影响力具有重要意义。 [ 方法 / 过程 ] 以情报学为例,在无标注跨学科语料下,基于获取的权威科学术语知识体系,借助字序列标注模型与远程监督算法获取跨学科知识扩散科学术语抽取与分类的学习语料,进而探讨基于深度学习的最优模型,从知识发现角度定义新科学术语判别规则,最后进行科学术语跨学科知识扩散的学科分布、引用章节位置、概念专指度等多维特征计算。[ 结果 / 结论 ]RoBERTa 模型在各项指标上整体表现最优,其调和平均值达到 98.08%,说明该算法能够保证跨学科知识扩散科学术语识别的可靠性和有效性。基于远程监督与深度学习的科学术语识别方法有利于挖掘跨学科知识扩散科学术语知识,可为跨学科知识扩散的智能知识挖掘提供领域化的基础计算资源支撑。多维特征计算能有效探究跨学科知识扩散科学术语交叉融合规律。

本文引用格式

孔玲 , 胡昊天 , 张卫 , 王东波 , 叶文豪 , 白如江 , 王效岳 . 跨学科知识扩散视域下学科交叉科学术语识别与特征计算[J]. 图书情报工作, 2024 , 68(12) : 119 -137 . DOI: 10.13266/j.issn.0252-3116.2024.12.010

Abstract

[Purpose/Significance] The scientific terms carry the basic knowledge and core concepts of disciplines. The knowledge mining and feature computation of scientific terms in transdisciplinary academic full-text citations are of great significance for in-depth investigation of the cross-fertilization pattern of transdisciplinary knowledge systems and transdisciplinary influence. [Method/Process] This paper took information science as an example. In the case of unlabeled transdisciplinary corpus, based on the acquired authoritative scientific terms knowledge system, it obtained the learning corpus of transdisciplinary knowledge diffusion of scientific terms extraction and classification with the help of word sequence annotation model and remote supervision. Then, it explored the optimal model based on deep learning, from the perspective of knowledge discovery, defined the new scientific terms discriminative rules, and finally it carried out the multidimensional feature computation such as discipline distribution, citation section distribution, concept specificity, etc., of the transdisciplinary knowledge diffusion of scientific terms. [Result/Conclusion] The overall performance of RoBERTa-based model is optimal in various indexes, with an F-score of 98.08%, which indicates that the algorithm can ensure the reliability and effectiveness of the identification of transdisciplinary knowledge diffusion scientific terms. The scientific terms recognition method based on remote supervision and deep learning is conducive to mining the knowledge of transdisciplinary knowledge diffusion scientific terms, which can provide domain-oriented basic computing resources support for intelligent knowledge mining of transdisciplinary knowledge diffusion. The multidimensional feature computation can effectively explore the cross-fertilization pattern of transdisciplinary knowledge diffusion scientific terms granularity.

参考文献

[1] STIRLING A. A general framework for analysing diversity in science, technology and society[J]. Journal of the royal society interface, 2007, 4(15):707-719.
[2] KIM H, PARK H, SONG M. Developing a topic-driven method for interdisciplinarity analysis[J]. Journal of informetrics, 2022, 16(2):101255.
[3] 莫雪盈,卢龙.引文视角下知识融合与论文社会影响力关系研究[J].信息资源管理学报, 2022, 12(6):133-141.(MO X Y, LU L. Research on the relationship between knowledge fusion and papers'social impact from the perspective of citation[J]. Journal of information resources management, 2022, 12(6):133-141.)
[4] MAO J, LIANG Z, CAO Y, et al. Quantifying cross-disciplinary knowledge flow from the perspective of content:introducing an approach based on knowledge memes[J]. Journal of informetrics, 2020, 14(4):101092.
[5] 李妙彤,刘芳华.术语特点及英汉翻译策略——以摄影文本为例[J].现代英语, 2021(13):55-57.(LI M T, LIU F H. Term characteristics and English-Chinese translation strategies:take photographic text as an example[J]. Modern english, 2021(13):55-57.)
[6] 冯志伟.术语学中的概念系统与知识本体[J].术语标准化与信息技术, 2006(1):9-15.(FENG Z W. Conception system and ontology in terminology[J]. Terminology standardization&information technology, 2006(1):9-15.)
[7] 胡雅敏,吴晓燕,陈方.基于机器学习的技术术语识别研究综述[J].数据分析与知识发现, 2022, 6(2/3):7-17.(HU Y M, WU X Y, CHEN F. Review of technology term recognition studies based on machine learning[J]. Data analysis and knowledge discovery, 2022, 6(2/3):7-17.)
[8] 卢超,章成志,王玉琢,等.语义特征分析的深化——科学文献的全文计量分析研究综述[J].中国图书馆学报, 2021, 47(2):110-131.(LU C, ZHANG C Z, WANG Y Z, et al. Strengthened analyses of semantic features:review of fulltext bibliometrics of academic documents[J]. Journal of library science in China, 2021, 47(2):110-131.)
[9] 胡志刚.全文引文分析方法与应用[D].大连:大连理工大学, 2014.(HU Z G. Full-text citation analysis and applications[D]. Dalian:Dalian University of Technology, 2014.)
[10] 陈必坤,刘钰馨,白宽,等.基于科学建模的学科交叉测度研究综述[J].图书情报工作, 2022, 66(18):126-139.(CHEN B K, LIU Y X, BAI K, et al. Literature review of interdisciplinary measurement based on modeling of science[J]. Library and information service, 2022, 66(18):126-139.)
[11] 孔玲,张卫,王东波,等.全文本引文视角下我国情报学跨学科知识扩散研究[J].文献与数据学报, 2022, 4(4):110-128.(KONG L, ZHANG W, WANG D B, et al. Interdisciplinary knowledge diffusion in information science of China from the perspective of full-text citation[J]. Journal of library and data, 2022, 4(4):110-128.)
[12] WANG Q, SCHNEIDER J W. Consistency and validity of interdisciplinarity measures[J]. Quantitative science studies, 2019, 1(2):1-28.
[13] PORTER A L, COHEN A S, ROESSNER J D, et al. Measuring researcher interdisciplinarity[J]. Scientometrics, 2007, 72(1):117-147.
[14] ZHANG L, ROUSSEAU R, GLÄNZEL W. Diversity of references as an indicator of the interdisciplinarity of journals:taking similarity between subject fields into account[J]. Journal of the Association for Information Science and Technology, 2016, 67(5):1257-1265.
[15] 荣国阳,李长玲,王欣欣,等.跨学科推动力视角下情报学对社会科学的知识输出及作用分析[J].图书情报工作, 2022, 66(23):13-20.(RONG G Y, LI C L, WANG X X, et al. Analysis on the knowledge output and its function of information science to social sciences:from the perspective of interdisciplinary impetus[J]. Library and information service, 2022, 66(23):13-20.)
[16] RAFOLS I, MEYER M. Diversity and network coherence as indicators of interdisciplinarity:case studies in bionanoscience[J]. Scientometrics, 2009, 82(2):263-287.
[17] 邱均平,李小涛.基于引文网络挖掘和时序分析的知识扩散研究[J].情报理论与实践, 2014, 37(7):5-10.(QIU J P, LI X T. Knowledge diffusion research based on citation network mining and timing analysis[J]. Information studies:theory&application, 2014, 37(7):5-10.)
[18] 徐璐,李长玲,荣国阳.期刊的跨学科引用对跨学科知识输出的影响研究——以图书情报领域为例[J].情报杂志, 2021, 40(7):182-188.(XU L, LI C L, RONG G Y. Research on the impact of periodical interdisciplinary citation on interdisciplinary knowledge output:taking library and information science for example[J]. Journal of intelligence, 2021, 40(7):182-188.)
[19] YAN E, DING Y, CRONIN B, et al. A bird's-eye view of scientific trading:dependency relations among fields of science[J]. Journal of informetrics, 2013, 7(2):249-264.
[20] YAN E. Finding knowledge paths among scientific disciplines[J]. Journal of the Association for Information Science and Technology, 2014, 65(11):2331-2347.
[21] 赵俊玲,刘尧.基于定量分析的我国图书情报学学科辐射力研究[J].情报理论与实践, 2014, 37(10):40-44.(ZHAO J L, LIU Y. Research on discipline radiation power of library and information science based on quantitative analysis[J]. Information studies:theory&application, 2014, 37(10):40-44.)
[22] 徐晴.我国图书情报学跨学科知识转移态势研究[J].图书情报知识, 2016(3):96-102.(XU Q. Research on the interdisciplinary knowledge transfer situation in library and information science in China[J]. Documentation, information&knowledge, 2016(3):96-102.)
[23] 宋凯,李秀霞,赵思喆,等.我国图书情报学文献的国际知识影响力分析[J].情报理论与实践, 2017, 40(7):38-42, 55.(SONG K, LI X X, ZHAO S Z, et al. International knowledge influence of Chinese information science&library science literature[J]. Information studies:theory&application, 2017, 40(7):38-42, 55.)
[24] 李林,李秀霞,刘超,等.跨学科知识贸易动态影响和扩散模式研究——以图书情报学和管理学为例[J].情报杂志, 2017, 36(2):182-186, 158.(LI L, LI X X, LIU C, et al. Research on trade dynamic impact and diffusion model of cross disciplinary knowledge:a case study of library and information science and management[J]. Journal of intelligence, 2017, 36(2):182-186, 158.)
[25] 刘超,李秀霞,邵作运.国内图书情报学与新闻传播学间学科影响度和交叉度分析——基于期刊引文分析[J].情报杂志, 2017, 36(7):111-115, 95.(LIU C, LI X X, SHAO Z Y. Analysis on the influence degree and the interdisciplinary degree between'library and information science'and'journalism and communication'in China:based on the analysis of journal citation[J]. Journal of intelligence, 2017, 36(7):111-115, 95.)
[26] 邵瑞华,李亮,刘勐.学科交叉程度与文献学术影响力的关系研究——以图书情报学为例[J].情报杂志, 2018, 37(3):146-151.(SHAO R H, LI L, LIU M. Research on relationship between interdisciplinary degree and academic impact of papers:taking the library and information science (LIS) as an example[J]. Journal of intelligence, 2018, 37(3):146-151.)
[27] 张艺蔓,马秀峰,程结晶.融合引文内容和全文本引文分析的知识流动研究[J].情报杂志, 2015, 34(11):50-54, 49.(ZHANG Y M, MA X F, CHENG J J. Research of knowledge flows based on citation content analysis[J]. Journal of intelligence, 2015, 34(11):50-54, 49.)
[28] ZHANG C Z, LIU L F, WANG Y Z. Characterizing references from different disciplines:a perspective of citation content analysis[J]. Journal of informetrics, 2021, 15(2):101134.
[29] 刘丽帆,张恒,章成志.基于学术文献引文内容的跨学科知识流动研究[J].情报理论与实践, 2022, 45(6):24-31, 47.(LIU L F, ZHANG H, ZHANG C Z. Interdisciplinary knowledge flows based on citation contents of academic papers[J]. Information studies:theory&application, 2022, 45(6):24-31, 47.)
[30] 徐庶睿,卢超,章成志.术语引用视角下的学科交叉测度——以PLOS ONE上六个学科为例[J].情报学报, 2017, 36(8):809-820.(XU S R, LU C, Zhang C Z. Measurement of interdisciplinary research from the perspective of terminology citation:six disciplines on PLOS ONE[J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(8):809-820.)
[31] 张卫,王昊,邓三鸿,等.面向数字人文的古诗文本情感术语抽取与应用研究[J].中国图书馆学报, 2021, 47(4):113-131.(ZHANG W, WANG H, DENG S H, et al. Sentiment term extraction and application of Chinese ancient poetry text for digital humanities[J]. Journal of library science in China, 2021, 47(4):113-131.)
[32] 郑亚琴,王晓宇,郑文生.微博口碑营销特征对企业品牌价值的影响——基于关系视角的研究[J].财贸研究, 2016, 27(4):120-126.(ZHENG Y Q, WANG X Y, ZHENG W S. Influence of characteristics of word-of-mouth marketing of Micro-blog on enterprise's brand value:from the perspective of relationship[J]. Finance and trade research, 2016, 27(4):120-126.)
[33] 刘素华.论手机自动记录用户行动轨迹与个人信息保护[J].法学评论, 2020, 38(5):101-111.(LIU S H. On the automatic recording of users'action trajectory by mobile phones and personal information protection[J]. Law review, 2020, 38(5):101-111.)
[34] 赵竞,孙晓军,周宗奎,等.网络交往中的人际信任[J].心理科学进展, 2013, 21(8):1493-1501.(ZHAO J, SUN X J, ZHOU Z K, et al. Interpersonal trust in online communication[J]. Advances in psychological science, 2013, 21(8):1493-1501.)
[35] 吴钢,赵一鸣,王瑜.传承文化泽惠世人《中国大百科全书》第三版图书馆学、情报学编纂工作开启![J].图书情报知识, 2014(6):130, 129.(WU G, ZHAO Y M, WANG Y. Passing on the culture and benefiting the world the 3rd edition of the Chinese encyclopedia of library and information science opens for compilation![J]. Documentation, information&knowledge, 2014(6):130, 129.)
[36] 杨穗珠,刘艳霞,张凯文,等.远程监督关系抽取综述[J].计算机学报, 2021, 44(8):1636-1660.(YANG S Z, LIU Y X, ZHANG K W, et al. Survey on distantly-supervised relation extraction[J]. Chinese journal of computers, 2021, 44(8):1636-1660.)
[37] SUN Y, WANG L, CHEN C, et al. Improved distant supervised model in Tibetan relation extraction using ELMo and attention[J]. IEEE access, 2019, 7(1):173054-173062.
[38] LIANG C, YU Y, JIANG H, et al. Bond:BERT-assisted opendomain named entity recognition with distant supervision[C]//Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery&data mining. 2020:1054-1064.
[39] 苏兆婧,余隋怀,初建杰,等.需求驱动的云平台产品关键设计特征识别方法[J].计算机集成制造系统, 2021, 27(12):3604-3613.(SU Z Q, YU S H, CHU J J, et al. Requirementdriven recognition method for key design features of products in cloud platform[J]. Computer integrated manufacturing system, 2021, 27(12):3604-3613.)
[40] 王乐.基于深度学习的中医临床病历事件抽取方法研究[D].北京:北京交通大学, 2022.(WANG L. Research on event extraction method of TCM clinical records based on deep learning[D]. Beijing:Beijing Jiaotong University, 2022.)
[41] SETTLES B, CRAVEN M. An analysis of active learning strategies for sequence labeling tasks[C]//Proceedings of the 2008 conference on empirical methods in natural language processing. Honolulu:Association for Computational Linguistics, 2008:1070-1079.
[42] CUI Y, CHE W, LIU T, et al. Pre-training with whole word masking for Chinese BERT[J]. IEEE/AC M transactions on audio, speech, and language processing, 2021, 29(1):3504-3514.
[43] 徐润华,王东波,刘欢,等.面向古籍数字人文的《资治通鉴》自动摘要研究——以SikuBERT预训练模型为例[J].图书馆论坛, 2022, 42(12):129-137.(XU R H, WANG D B, LIU H, et al. Automatic summarization of ZiZhi TongJian from the perspective of digital humanities based on ancient Chinese books:a case of SikuBERT pre-training model[J]. Library tribune, 2022, 42(12):129-137.)
[44] 刘江峰,王希羽,张君冬,等.领域文献深层语义特征视角下的期刊新兴研究主题发现[J].情报理论与实践, 2024, 47(3):177-187.(LIU J F, WANG X Y, ZHANG J D, et al. Emergent research topic discovery in journals from the perspective of deep semantic features of domain literature[J]. Information studies:theory&application, 2024, 47(3):177-187.)
[45] SHEN S, YAN D Y, LIU J F, et al. Pre-trained language model for the humanities and social sciences in Chinese[C]//Information processing&management Conference. Amsterdam:Elsevier, 2022.
[46] 官琴.基于深度学习的我国情报学理论方法术语识别研究[D].南京:南京大学, 2019.(GUAN Q. Research on terminology recognition of Chinese information science theory and methodology based on deep learning[D]. Nanjing:Nanjing University, 2019.)
[47] 王昊,邓三鸿,苏新宁,等.基于深度学习的情报学理论及方法术语识别研究[J].情报学报, 2020, 39(8):817-828.(WANG H, DENG S H, SU X N, et al. A study on Chinese terminology recognition of theory and method from information science:based on deep learning[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(8):817-828.)
[48] 肖连杰,孟涛,王伟,等.基于深度学习的情报分析方法识别研究——以安全情报领域为例[J].数据分析与知识发现, 2019, 3(10):20-28.(XIAO L J, MENG T, WANG W, et al. Entity recognition of intelligence method based on deep learning:taking area of security intelligence for example[J]. Data analysis and knowledge discovery, 2019, 3(10):20-28.)
[49] 帅博威.基于MeSH的医学文献主题分类与可视化研究[D].成都:电子科技大学, 2019.(SHUAI B W. Medical literature topic classification and visualization based on MeSH[D]. Chengdu:University of Electronic Science and Technology of China, 2019.)
[50] VALDERRAMA-ZURIÁN J C, GARCÍA-ZORITA C, MARUGÁN-LÁZARO S, et al. Comparison of MeSH terms and keywords plus terms for more accurate classification in medical research fields. A case study in cannabis research[J]. Information processing&management, 2021, 58(5):102658.
[51] 梁瑞文,毛进,芦昆,等.生物医学领域技术方法的知识增长模式研究[J].情报理论与实践, 2023, 46(8):104-112.(LIANG R W, MAO J, LU K, et al. Research on knowledge growth mode of technical methods in the biomedical field[J]. Information studies:theory&application, 2023, 46(8):104-112.)
[52] 强韶华.中国企业知识管理系统实施的现状与对策[J].南京工业大学学报(社会科学版), 2003(4):65-68.(QIANG S H. The actuality and countermeasures of implementing knowledge management system in Chinese enterprises[J]. Journal of Nanjing University of Technology (social science edition), 2003(4):65-68.)
[53] 曹树金,岳文玉.基于深度学习的中共党史文献命名实体识别研究[J].情报资料工作, 2022, 43(5):81-88.(CAO S J, YUE W Y. Research on named entity recognition of the documents of history of the communist party of China based on deep learning[J]. Information and documentation services, 2022, 43(5):81-88.)
[54] 胡昊天,王东波,邓三鸿,等.基于情报学招聘实体挖掘的情报学教育及人才培养分析[J].情报理论与实践, 2021, 44(1):8-17.(HU H T, WANG D B, DENG S H, et al. Analyzing the information science education and training talents based on mining the information science recruitment entity[J]. Information studies:theory&application, 2021, 44(1):8-17.)
[55] 刘渊晨,王昊,高亚琪.在线音乐歌单播放量预测及影响因素分析[J].数据分析与知识发现, 2021, 5(8):100-112.(LIU Y C, WANG H, GAO Y Q. Predicting online music playbacks and influencing factors[J]. Data analysis and knowledge discovery, 2021, 5(8):100-112.)
[56] 胡昊天,吉晋锋,王东波,等.基于深度学习的食品安全事件实体一体化呈现平台构建[J].数据分析与知识发现, 2021, 5(3):12-24.(HU H T, JI J F, WANG D B, et al. An integrated platform for food safety incident entities based on deep learning[J]. Data analysis and knowledge discovery, 2021, 5(3):12-24.)
[57] 张卫,王昊,李晓敏,等.数字人文视角下古诗意象知识抽取及其文化图式构建研究[J].图书情报工作, 2022, 66(24):104-117.(ZHANG W, WANG H, LI X M, et al. Knowledge extraction and cultural schema construction of classical poetry imagery from the digital humanities[J]. Library and information service, 2022, 66(24):104-117.)
[58] 苏新宁.大数据时代情报学学科崛起之思考[J].情报学报, 2018, 37(5):451-459.(SU X N. The rise of intelligence studies in the age of big Data[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(5):451-459.)
[59] 朱少强,邱均平.文献计量与内容分析——文献群中隐含信息的挖掘[J].图书情报工作, 2005(6):19-23.(ZHU S Q, QIU J P. Bibliometrics and content analysis:finding latent semantics behind documents[J]. Library and information service, 2005(6):19-23.)
[60] 杨宁,张志强.结合计量分析和内容分析的科学数据集使用特征研究[J].图书情报工作, 2022, 66(10):122-130.(YANG N, ZHANG Z Q. Research on the use characteristics of scientific datasets combined with quantitative analysis and content analysis[J]. Library and information service, 2022, 66(10):122-130.)
[61] 王曰芬,路菲,吴小雷.文献计量和内容分析的比较与综合研究[J].图书情报工作, 2005(9):72-75.(WANG Y F, LU F, WU X L. Comparative and synthetic research on bibliometrics and content analysis[J]. Library and information service, 2005(9):72-75.)
[62] 谢珍,马建霞,胡文静.基于多维度引用特征的学术论文评价研究[J].情报理论与实践, 2023, 46(8):51-58, 76.(XIE Z, MA J X, HU W J. Research on the evaluation of academic paper based on multidimensional citation characteristics[J]. Information studies:theory&application, 2023, 46(8):51-58, 76.)
[63] VOOS H, DAGAEV K. Are all citations equal?Or, did we op.cit. your idem?[J]. Journal of academic librarianship, 1976, 6(1):19-21.
[64] SOMBATSOMPOP N, KOSITCHAIYONG A, MARKPIN T, et al. Scientific evaluations of citation quality of international research articles in the SCI database:Thailand case study[J]. Scientometrics, 2006, 66(3):521-535.
[65] 魏绪秋,姜召昊,常霞,等.基于引证意图的学术论文创新性评价研究[J].情报理论与实践, 2023, 46(9):24-30, 46.(WEI X Q, JIANG Z H, CHANG X, et al. Research on the innovation evaluation of academic papers based on citing intention[J]. Information studies:theory&application, 2023, 46(9):24-30, 46.)
[66] 鲍彤,章成志. ChatGPT中文信息抽取能力测评——以三种典型的抽取任务为例[J].数据分析与知识发现, 2023, 7(9):1-11.(BAO T, ZHANG C Z. Extracting Chinese information with ChatGPT:an empirical study by three typical tasks[J]. Data analysis and knowledge discovery, 2023, 7(9):1-11.)
[67] 张颖怡,章成志,周毅,等.基于ChatGPT的多视角学术论文实体识别:性能测评与可用性研究[J].数据分析与知识发现, 2023, 7(9):12-24.(ZHANG Y Y, ZHANG C Z, ZHOU Y, et al. ChatGPT-based scientific paper entity recognition:performance measurement and availability research[J]. Data analysis and knowledge discovery, 2023, 7(9):12-24.)
[68] 刘江峰,刘雏菲,齐月,等. AIGC助力数字人文研究的实践探索:SikuGPT驱动的古诗词生成研究[J].情报理论与实践, 2023, 46(5):23-31.(LIU J F, LIU C F, QI Y, et al. A practical exploration of AIGC-powered digital humanities research:a SikuGPT driven research of ancient poetry generation[J]. Information studies:theory&application, 2023, 46(5):23-31.)
文章导航

/