文献中的链接将文献与数据、代码、文档、网页等科技资源相关联,资源链接引用的上下文信息反映了科研活动中科研主体与科技资源形成的关系。该文通过对文献中的链接信息进行细粒度分析,提出了一种对其关联的科技资源种类和引用目的进行知识建模的方法,并在大规模文献数据集上进行了实证。同时从国内外科技资源的利用情况出发,对科技资源的重要程度、发展方向、使用风险等进行了深入的探索。该文可为了解国内外前沿技术进展,以及我国科研活动中科技资源风险评估判定提供科学依据,且对于自然语言处理领域中对科技文献文本的分析研究具有重大意义。
Abstract
The hyperlinks of resources in scientific literature include data, code, documents, and web pages. The citation context information of the resources reflects the relationship between scientific research subjects and scientific resources in scientific research activities. Based on the fine-grained analysis of the citation information in the literature, this paper proposes a novel knowledge modeling method to characterize the resource categories and the resource citation purposes, and conducts an empirical evaluation on large-scale scientific literature datasets. Upon detailed analysis into the utilization of scientific resources at home and abroad, we explore the importance, the direction of development and the risks of use for such resources. This framework can be used to understand the progress of the advanced technologies at home and abroad, and can further provide scientific evidence for the assessment of the fateful resources in China’s scientific research activities.
关键词
科技资源风险评估 /
文献 /
链接信息
{{custom_keyword}} /
Key words
scientific resource link assessment /
literature /
link information
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Feng S, Wang Y, Liu L, et al. Attention based hierarchical LSTM network for context-aware microblog sentiment classification[J].World Wide Web, 2019, 22(1):59-81.
[2] Lample G, Ballesteros M, Subramanian S, et al. Neural architectures for named entity recognition[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics, 2016.
[3] Zhang D, Wang D. Relation classification via recurrent neural network[J].arXiv preprint arXiv:1508.01006, 2015.
[4] Luan Y, Ostendorf M, Hajishirzi H. Scientific information extraction with semi-supervised neural tagging[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017.
[5] Zhao H, Luo Z, Feng C, et al. A context-based framework for resource citation classification in scientific literatures[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Deve-lopment in Information Retrieval,2019.
[6] Zhao H, Luo Z, Feng C, et al. A context-based framework for modeling the role and function of on-line resource citations in scientific literature[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing,2019.
[7] Teufel S .Automatic classification of citation function[C]//Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, Australia. Association for Computational Linguistics, 2006.
[8] Jurgens, David, et al.Measuring the evolution of a scientific field through citation frames[J].Transactions of the Association for Computational Linguistics, 2018(6):391-406.
[9] Tang J, Zhang J.A discriminative approach to topic-based citation recommendation[C]//Proceedings of Pacific-Asia Conference on Advances in Knowledge Discovery & Data Mining, 2009.
[10] He Q, Pei J, Kifer D, et al. Context-aware Citation Recommendation[C]//Proceedings of the International Conference on World Wide Web. DBLP, 2010.
[11] Huang W, Wu Z, Chen L, et al. A neural probabilistic model for context based citation recommendation[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence, 2015.
[12] Teufel, Simone. Argumentative zoning: Information extraction from scientific text[D]. Edinburgh: University of Edinburgh,1999.
[13] Mochales R, Moens M F. Argumentation mining[J]. Artificial Intelligence & Law, 2011, 19(1):1-22.
[14] Hachey B, Grover C. Extractive summarisation of legal texts[J]. Artificial Intelligence & Law, 2006, 14(4):305-345.
[15] Mizuta Y, Korhonen A, Mullen T, et al. Zone analysis in biology articles as a basis for information extraction[J]. International Journal of Medical Informatics, 2006, 75(6):468-487.
[16] Teufel S, Siddharthan A, Batchelor C. Towards discipline-independent argumentative zoning: Evidence from chemistry and computational linguistics[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2009.
[17] Teufel S, Moens M. Summarizing scientific articles: Experiments with relevance and rhetorical status[J]. Computational Linguistics, 2002, 28(4):409-445.
[18] Feng V W, Hirst G. Classifying arguments by scheme[C]//Proceedings of the Meeting of the Association for Computational Linguistics: Human Language Technologies, 2012.
[19] Florou, Eirini, et al. Argument extraction for supporting public policy formulation[C]//Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, 2013.
[20] Stab Christian, Iryna Gurevych. Annotating Argument Components and Relations in Persuasive Essays[C]//Proceedings of the 25th International Conference on Computational Linguistics. 2014.
[21] Liakata M, Saha S, Dobnik S, et al. Automatic recognition of conceptualization zones in scientific articles and two life science applications[J]. Bioinformatics, 2012, 28(7):991-1000.
[22] Jimeno-Yepes, Antonio, et al. Using the argumentative structure of scientific literature to improve information access[C]//Proceedings of the 2013 Workshop on Biomedical Natural Language Processing, 2013.
[23] Duck G, Nenadic G, Brass A, et al. bioNerDS: Exploring bioinformatics’ database and software use through literature mining[J]. BMC Bioinformatics, 2013, 14(1):194-194.
[24] Duck G, Nenadic G, Brass A, et al. Extracting patterns of database and software usage from the bioinformatics literature[J]. Bioinformatics, 2014, 30(17):601-608.
[25] Geraint D, Goran N, Michele F, et al. A survey of bioinformatics database and software usage through mining the literature[J]. PLoS ONE, 2016, 11(6):1-25.
[26] Toshihisa T, Yasunori Y. OReFiL: An online resource finder for life sciences[J]. BMC Bioinformatics, 2007, 8(1):1-81.
[27] Calle G D L, Miguel García-Remesal, Chiesa S, et al. BIRI: A new approach for automatically discovering and indexing available public bioinformatics resources from the literature[J]. BMC Bioinformatics, 2009, 10(1):320.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家自然科学基金(61976221,61602490)
{{custom_fund}}
PDF(4588 KB)
