Please wait a minute...
Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (6): 60-68    DOI: 10.11925/infotech.2096-3467.2019.0487
Current Issue | Archive | Adv Search |
A Deep Learning-based Method of Argumentative Zoning for Research Articles
Wang Mo,Cui Yunpeng(),Chen Li,Li Huan
Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China
Key Laboratory of Big Agri-data, Ministry of Agriculture and Rural Areas, Beijing 100081, China
Download: PDF(1458 KB)   HTML ( 14
Export: BibTeX | EndNote (RIS)      

[Objective] This study aims at developing a new argumentative zoning method based on deep learning language representation model to achieve better performance. [Methods] We adopted a pre-trained deep learning language representation model BERT, and improved model input with sentence position feature to conduct transfer learning on training data from biochemistry journals. The learned sentence representations were then fed into neural network classifier to achieve argumentative zoning classification. [Results] The experiment indicated that for the eleven-class task, the method achieved significant improvement for most classes. The accuracy reached 81.3%, improved by 29.7% compared to the best performance from previous studies. For the seven core classes, the model achieved an accuracy of 85.5%. [Limitations] Due to limitation on experiment environment, our refined model was trained based on pre-trained parameters, which could limit the potential for classification performance. [Conclusions] The proposed method showed significant improvement compared to shallow machine learning schema or original BERT model, and was able to avoid tedious work of feature engineering. The method is independent of language, hence also suitable for research articles in Chinese language.

Key wordsArgumentative Zoning      Deep Learning      Bidirectional Encoder      Neural Networks     
Received: 09 May 2019      Published: 18 May 2020
ZTFLH:  TP391  
Corresponding Authors: Cui Yunpeng     E-mail:

Cite this article:

Wang Mo,Cui Yunpeng,Chen Li,Li Huan. A Deep Learning-based Method of Argumentative Zoning for Research Articles. Data Analysis and Knowledge Discovery, 2020, 4(6): 60-68.

URL:     OR

An Example of Move Structure of Research Articles
Deep Learning Classification Model Structure for Argumentative Zoning
Input of Proposed Argumentative Zoning Model
An Example of Sentence Position Embeddings
Multilayer Perceptron Classifier for Argumentative Zoning
类别 类别缩写 中文含义
Conclusion CON 结论
Result RES 结果
Goal GOA 目标
Method MET 方法
Object OBJ 对象
Experiment EXP 实验
Observation OBS 观察
Hypothesis HYP 假设
Motivation MOT 动机
Background BAC 背景
Model MOD 模型
Move Structure Classes of ART Corpus Dataset
Example of Preprocessed Data
句子数 3 082 7 349 548 3 740 1 189 2 822 4 643 655 465 6 648 3 449
占比(%) 8.91 21.25 1.58 10.81 3.44 8.16 13.42 1.89 1.35 19.22 9.97
平均单词数 28.10 26.70 28.46 25.07 25.16 24.33 22.81 27.33 25.39 25.50 27.16
Statistics of the Dataset for Each Move Structure Class
分类模型 批处理大小 学习率 训练期 分类器隐含层节点数
11标签分类 16 2e-5 4 256
7标签分类 32 2e-5 4 128
Hyper-parameters of Optimum Models
分类模型 总体
LibSVM 11标签 51.6 43.0 46.3
11标签分类 SciBERT 75.2 68.5 74.6
改进输入 81.3 72.4 75.5
7标签分类 SciBERT 80.1 76.4 78.8
改进输入 85.5 80.7 83.1
Classification Results of Different Argumentative Zoning Models
Classification Metrics on 11-class Argumentative Zoning for Each Class(%)
[1] 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.
doi: 10.1093/bioinformatics/bts071
[2] Teufel S, Moens M. Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status[J]. Computational Linguistics, 2002,28(4):409-445.
doi: 10.1162/089120102762671936
[3] 王立非, 刘霞. 英语学术论文摘要语步结构自动识别模型的构建[J]. 外语电化教学, 2017(2):45-50,64.
[3] ( Wang Lifei, Liu Xia. Constructing a Model for the Automatic Identification of Move Structure in English Research Article Abstracts[J]. Technology Enhanced Foreign Language Education, 2017(2):45-50, 64.)
[4] Guo Y, Korhonen A, Poibeau T. A Weakly-Supervised Approach to Argumentative Zoning of Scientific Documents [C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011: 273-283.
[5] 孟愉, 伍兴权. 语步分析视角下的等离子体物理国际SCI期刊论文写作范式研究[J]. 上海理工大学学报(社会科学版), 2018,40(3):201-206.
[5] ( Meng Yu, Wu Xingquan. Writing Paradigm of Plasma Physics SCI Journal Articles from the Perspective of Move Analysis Theory[J]. Journal of University of Shanghai for Science and Technology(Social Science) , 2018,40(3):201-206.)
[6] Teufel S, Carletta J, Moens M. An Annotation Scheme for Discourse-Level Argumentation in Research Articles [C]//Proceedings of the 9th Conference on European Chapter of the Association for Computational Linguistics. 1999: 110-117.
[7] 徐昉. 英语学术语篇语类结构研究述评(1980-2012)[J]. 东南大学学报(哲学社会科学版), 2013,15(5):128-133.
[7] ( Xu Fang. A Survey on English Academic Paper Genre Studies[J]. Journal of Southeast University (Philosphy and Social Science), 2013,15(5):128-133.)
[8] Nasar Z, Jaffry S W, Malik M K. Information Extraction from Scientific Articles: A Survey[J]. Scientometrics, 2018,117(3):1931-1990.
doi: 10.1007/s11192-018-2921-5
[9] Gupta S, Manning C D. Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers [C]//Proceedings of the 5th International Joint Conference on Natural Language Processing. 2011: 1-9.
[10] Houngbo H, Mercer R E. Method Mention Extraction from Scientific Research Papers [C]//Proceedings of COLING 2012. 2012:1211-1222.
[11] Ruch P, Boyer C, Chichester C, et al. Using Argumentation to Extract Key Sentences from Biomedical Abstracts[J]. International Journal of Medical Informatics, 2007,76(3):195-200.
doi: 10.1016/j.ijmedinf.2006.05.002
[12] Lakhanpal S, Gupta A, Agrawal R. Towards Extracting Domains from Research Publications [C]// Proceedings of MAICS 2015. 2015:117-120.
[13] Lin J, Karakos D, Demner-Fushman D, et al. Generative Content Models for Structural Analysis of Medical Abstracts [C]//Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology. 2006: 65-72.
[14] Wu J C, Chang Y C, Liou H C, et al. Computational Analysis of Move Structures in Academic Abstracts [C]//Proceedings of the COLING/ACL on Interactive Presentation Sessions. 2006: 41-44.
[15] Hirohata K, Okazaki N, Ananiadou S, et al. Identifying Sections in Scientific Abstracts Using Conditional Random Fields [C]//Proceedings of the 3rd International Joint Conference on Natural Language Processing: Volume-I. 2008: 381-388.
[16] Lin S, Ng J P, Pradhan S, et al. Extracting Formulaic and Free Text Clinical Research Articles Metadata Using Conditional Random Fields [C]//Proceedings of the NAACL HLT 2010 2nd Louhi Workshop on Text and Data Mining of Health Documents. 2010: 90-95.
[17] Ronzano F, Saggion H. Dr. Inventor Framework: Extracting Structured Information from Scientific Publications [C]//Proceedings of the International Conference on Discovery Science. 2015: 209-220.
[18] Anthony L, Lashkia G V. Mover: A Machine Learning Tool to Assist in the Reading and Writing of Technical Papers[J]. IEEE Transactions on Professional Communication, 2003,46(3):185-193.
doi: 10.1109/TPC.2003.816789
[19] Guo Y, Korhonen A, Liakata M, et al. Identifying the Information Structure of Scientific Abstracts: An Investigation of Three Different Schemes [C]//Proceedings of the 2010 Workshop on Biomedical Natural Language Processing. 2010: 99-107.
[20] Dayrell C, Candido Jr A, Lima G, et al. Rhetorical Move Detection in English Abstracts: Multi-label Sentence Classifiers and Their Annotated Corpora [C]//Proceedings of the 8th International Conference on Language Resources and Evaluation. 2012: 1604-1609.
[21] Liu H. Automatic Argumentative-Zoning Using Word2vec [OL]. arXiv Preprint, arXiv: 1703. 10152.
[22] 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. 2013: 3111-3119.
[23] Pennington J, Socher R, Manning C. Glove: Global Vectors for Word Representation [C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014: 1532-1543.
[24] Devlin J, Chang M-W, Lee K, et al. Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding[OL]. arXiv Preprint, arXiv: 1810. 04805.
[25] Vaswani A, Shazeer N, Parmar N, et al. Attention is All You Need[OL]. arXiv Preprint, arXiv: 1706. 03762.
[26] Beltagy I, Lo K, Cohan A. SciBERT: A Pretrained Language Model for Scientific Text [C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019: 3606-3611.
[1] Jiao Qihang,Le Xiaoqiu. Generating Sentences of Contrast Relationship[J]. 数据分析与知识发现, 2020, 4(6): 43-50.
[2] Liu Weijiang,Wei Hai,Yun Tianhe. Evaluation Model for Customer Credits Based on Convolutional Neural Network[J]. 数据分析与知识发现, 2020, 4(6): 80-90.
[3] Deng Siyi,Le Xiaoqiu. Coreference Resolution Based on Dynamic Semantic Attention[J]. 数据分析与知识发现, 2020, 4(5): 46-53.
[4] Yu Chuanming,Yuan Sai,Zhu Xingyu,Lin Hongjun,Zhang Puliang,An Lu. Research on Deep Learning Based Topic Representation of Hot Events[J]. 数据分析与知识发现, 2020, 4(4): 1-14.
[5] Su Chuandong,Huang Xiaoxi,Wang Rongbo,Chen Zhiqun,Mao Junyu,Zhu Jiaying,Pan Yuhao. Identifying Chinese / English Metaphors with Word Embedding and Recurrent Neural Network[J]. 数据分析与知识发现, 2020, 4(4): 91-99.
[6] Liu Tong,Ni Weijian,Sun Yujian,Zeng Qingtian. Predicting Remaining Business Time with Deep Transfer Learning[J]. 数据分析与知识发现, 2020, 4(2/3): 134-142.
[7] Chuanming Yu,Haonan Li,Manyi Wang,Tingting Huang,Lu An. Knowledge Representation Based on Deep Learning:Network Perspective[J]. 数据分析与知识发现, 2020, 4(1): 63-75.
[8] Mengji Zhang,Wanyu Du,Nan Zheng. Predicting Stock Trends Based on News Events[J]. 数据分析与知识发现, 2019, 3(5): 11-18.
[9] Jingjing Pei,Xiaoqiu Le. Identifying Coordinate Text Blocks in Discourses[J]. 数据分析与知识发现, 2019, 3(5): 51-56.
[10] Zhixiong Zhang,Huan Liu,Liangping Ding,Pengmin Wu,Gaihong Yu. Identifying Moves of Research Abstracts with Deep Learning Methods[J]. 数据分析与知识发现, 2019, 3(12): 1-9.
[11] Li Yu,Li Qian,Changlei Fu,Huaming Zhao. Extracting Fine-grained Knowledge Units from Texts with Deep Learning[J]. 数据分析与知识发现, 2019, 3(1): 38-45.
[12] Changlei Fu,Li Qian,Huaping Zhang,Huaming Zhao,Jing Xie. Mining Innovative Topics Based on Deep Learning[J]. 数据分析与知识发现, 2019, 3(1): 46-54.
[13] Bengong Yu,Peihang Zhang,Qingtang Xu. Selecting Products Based on F-BiGRU Sentiment Analysis[J]. 数据分析与知识发现, 2018, 2(9): 22-30.
[14] Yuemei Xu,Sining Lv,Lianqiao Cai,Xiaoya Zhang. Analyzing News Topic Evolution with Convolutional Neural Networks and Topic2Vec[J]. 数据分析与知识发现, 2018, 2(9): 31-41.
[15] Wei Lu,Mengqi Luo,Heng Ding,Xin Li. Image Annotation Tags by Deep Learning and Real Users: A Comparative Study[J]. 数据分析与知识发现, 2018, 2(5): 1-10.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938