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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (6): 60-68    DOI: 10.11925/infotech.2096-3467.2019.0487
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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
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Abstract  

[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: cuiyunpeng@caas.cn

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:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0487     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I6/60

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
统计指标 CON RES GOA MET OBJ EXP OBS HYP MOT BAC MOD
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
准确率(%)
平均
召回率(%)
平均F1(%)
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(%)
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