基于学术论文全文的研究方法句自动抽取研究

doi:10.3772/j.issn.1000-0135.2020.06.008

情报学报

2020, Vol. 39

Issue (6): 640-650 DOI: 10.3772/j.issn.1000-0135.2020.06.008

情报分析方法与技术

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基于学术论文全文的研究方法句自动抽取研究

张颖怡, 章成志

南京理工大学经济管理学院信息管理系，南京 210094

Methodological and Automatic Sentence Extraction from Academic Article s Full-text

Zhang Yingyi, Zhang Chengzhi

Department of Information Management, School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094

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摘要研究方法是科技文献中的重要内容，是解决学科领域问题的方法、工具、手段或技术。研究方法的描述通常以句子为单位。将分散在科技文献中的研究方法句进行汇总，可以辅助科研工作者快速地搜寻合适的研究方法。根据方法使用主体，将研究方法句进一步分为论文使用方法句和论文引用方法句。论文使用方法句是指论文中使用的研究方法的描述句。论文引用方法句是指论文对前人使用过的研究方法的描述句。本文使用多种基于神经网络的句子分类模型从科技文献全文本中进行研究方法句抽取。在模型词向量表示层，论文使用BERT和word2vec两种词向量模型。在模型的特征选择层，本文选用三种不同的网络，分别为卷积神经网络、双向长短时记忆网络和注意力机制网络。另外，论文使用两种模型训练方式，分别为单层次结构和两层次结构。实验结果表明，基于BERT的单层次结构的双向长短时记忆网络模型取得了较优的性能。本文从《情报学报》已发表论文中进行研究方法句的抽取并分析研究方法句的分布情况。分析发现，《情报学报》逐渐重视情报学中理论的发展并关注建设情报学学科的理论体系。

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	张颖怡
	章成志

关键词 ：研究方法句抽取, 信息抽取, 深度学习, BERT

收稿日期: 2019-10-13

基金资助:国家社会科学基金重大项目“情报学学科建设与情报工作未来发展路径研究”（17ZDA291）。

作者简介: 张颖怡，女，1992 年生，博士研究生，研究方向为自然语言处理与文本挖掘；章成志，男，1977 年生，博士，教授，博士生导师，研究方向为信息组织、信息检索、文本挖掘及自然语言处理，E-mail：zhangcz@njust.edu.cn。

引用本文:

张颖怡, 章成志. 基于学术论文全文的研究方法句自动抽取研究[J]. 情报学报, 2020, 39(6): 640-650.
Zhang Yingyi, Zhang Chengzhi. Methodological and Automatic Sentence Extraction from Academic Article s Full-text. 情报学报, 2020, 39(6): 640-650.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2020.06.008 或 https://qbxb.istic.ac.cn/CN/Y2020/V39/I6/640

1 Bornmann L, Mutz R. Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references[J]. Journal of the Association for Information Science and Technology, 2015, 66(11): 2215-2222.
2 Gupta S, Manning C. Analyzing the dynamics of research by extracting key aspects of scientific papers[C]// Proceedings of the 5th International Joint Conference on Natural Language Processing. Asian Federation of Natural Language Processing, 2011: 1-9.
3 Kova?evi? A, Konjovi? Z, Milosavljevi? B, et al. Mining methodologies from NLP publications: A case study in automatic terminology recognition[J]. Computer Speech & Language, 2012, 26(2): 105-126.
4 Singh M, Dan S, Agarwal S, et al. AppTechMiner: Mining applications and techniques from scientific articles[C]// Proceedings of the 6th International Workshop on Mining Scientific Publications. New York: ACM Press, 2017: 1-8.
5 蒋婷. 学科领域本体学习及学术资源语义标注研究[D]. 南京: 南京大学, 2017.
6 Hirohata K, Okazaki N, Ananiadou S, et al. Identifying sections in scientific abstracts using conditional random fields[C]// Proceedings of the Third International Joint Conference on Natural Language Processing, Hyderabad, India, 2008: 381-388.
7 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.
8 Hayes P J, Andersen P M, Nirenburg I B, et al. TCS: A shell for content-based text categorization[C]// Proceedings of the Sixth Conference on Artificial Intelligence for Applications. IEEE Press, 1990: 320-326.
9 华秀丽, 徐凡, 王中卿, 等. 细粒度科技论文摘要句子分类方法[J]. 计算机工程, 2012, 38(14): 138-140.
10 Nomponkrang T, Sanrach C. The comparison of algorithms for Thai-sentence classification[J]. International Journal of Information and Education Technology, 2016, 6(10): 801-808.
11 Yamamoto Y, Takagi T. A sentence classification system for multi biomedical literature summarization[C]// Proceedings of the 21st International Conference on Data Engineering Workshops. IEEE Press, 2005: 1163-1163.
12 Hsu S T, Moon C, Jones P, et al. A hybrid CNN-RNN alignment model for phrase-aware sentence classification[C]// Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017, 2: 443-449.
13 Limsopatham N, Collier N. Modelling the combination of generic and target domain embeddings in a convolutional neural network for sentence classification[C]// Proceedings of the 15th Workshop on Biomedical Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016: 136-140.
14 Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[OL]. https://arxiv.org/pdf/1412.3555v1.pdf.
15 Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2019, 1: 4171-4186.
16 Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2013, 2: 3111-3119.
17 Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2017: 6000-6010.
18 Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18(5-6): 602-610.
19 McCallum A, Nigam K. A comparison of event models for naive Bayes text classification[C]// Proceedings of the AAAI/ICML -98 Workshop on Learning for Text Categorization. Palo Alto: AAAI Press, 1998: 41-48.
20 Salton G, Buckley C. Term-weighting approaches in automatic text retrieval[J]. Information Processing & Management, 1988, 24(5): 513-523.
21 Keller J M, Gray M R, Givens J A. A fuzzy K-nearest neighbor algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(4): 580-585.
22 Joachims T. Text categorization with Support Vector Machines: Learning with many relevant features[C]// Proceedings of the European Conference on Machine Learning. Heidelberg: Springer, 1998: 137-142.
23 王芳, 陈锋, 祝娜, 等. 我国情报学理论的来源、应用及学科专属度研究[J]. 情报学报, 2016, 35(11): 1148-1164.