Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (5): 51-56    DOI: 10.11925/infotech.2096-3467.2018.1380
Current Issue | Archive | Adv Search |
Identifying Coordinate Text Blocks in Discourses
Jingjing Pei,Xiaoqiu Le
National Science Library, Chinese Academy of Sciences, Beijing 100190, China
Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
Download: PDF(662 KB)   HTML ( 5
Export: BibTeX | EndNote (RIS)      

[Objective] This paper proposes a method to identify the coordinate text blocks by semantic and layout features, which are distributed in different paragraphs. It also provides a pre-trained model for these knowledge objects. [Methods] First, we used each paragraph as a processing unit and added the layout features based on the character and word vectors. Then, we concatenated multi-dimensional features to represent each paragraph. Third, we employed the convolutional neural network (CNN) model to train the annotated data and obtained the recognition model for coordinate relationship text blocks. [Results] The proposed approach achieved a precision of 96% with manually annotated scientific papers, which was 3% higher than those of the baseline model. The recall was also improved by 2%. [Limitations] Our model can only work with HTML files. More research is needed to examine it with other data formats. [Conclusions] The proposed method is able to effectively identify coordinate text blocks in discourses, which can be used as a pre-trained model for coordinate knowledge objects.

Key wordsCoordinate Relationship      Text Representation      Text Block      Deep Learning     
Received: 06 December 2018      Published: 03 July 2019

Cite this article:

Jingjing Pei,Xiaoqiu Le. Identifying Coordinate Text Blocks in Discourses. Data Analysis and Knowledge Discovery, 2019, 3(5): 51-56.

URL:     OR

[1] Nivre J.Dependency Parsing[J]. Language & Linguistics Compass, 2010, 4(3): 138-152.
[2] 昝红英, 张静杰, 娄鑫坡. 汉语虚词用法在依存句法分析中的应用研究[J]. 中文信息学报, 2013, 27(5): 35-42.
[2] (Zan Hongying, Zhang Jingjie, Lou Xinpo.Studies on the Application of Chinese Functional Words’ Usages in Dependency Parsing[J]. Journal of Chinese Information Processing, 2013, 27(5): 35-42.)
[3] 王东波. 基于规则的单层单标记联合结构自动识别[J].文教资料, 2008(9): 29-31.
[3] (Wang Dongbo.Automatic Identification of Non-nest Coordinate Structure Based on Rules[J]. Data of Culture and Education, 2008(9): 29-31.)
[4] Magerman D M.Natural Language Parsing as Statistical Pattern Recognition[D]. California: Doctoral Dissertation Stanford University, 1994.
[5] 郑略省, 吕学强, 刘坤, 等. 汉语并列关系的识别研究[J].北京大学学报: 自然科学版, 2013, 49(1): 20-24.
[5] (Zheng Luesheng, Lv Xueqiang, Liu Kun, et al.Automatic Identification of Chinese Coordination Relations[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2013, 49(1): 20-24.)
[6] 石翠, 王杨, 杨彬, 等. 面向中文专利文献的单层并列结构识别[J]. 现代图书情报技术, 2014(10): 76-83.
[6] (Shi Cui, Wang Yang, Yang Bin, et al.Identification of Non-nest Coordination for Chinese Patent Literature[J]. New Technology of Library and Information Service, 2014(10): 76-83.)
[7] 苗艳军, 李军辉, 周国栋. 统计和规则相结合的并列结构自动识别[J]. 计算机应用研究, 2009, 26(9): 3403-3406.
[7] (Miao Yanjun, Li Junhui, Zhou Guodong.Automatic Identification of Coordinate Structure Based on Statistics and Rules[J]. Application Research of Computers, 2009, 26(9): 3403-3406.)
[8] Socher R, Lin C C, Manning C, et al.Parsing Natural Scenes and Natural Language with Recursive Neural Networks[C]// Proceedings of the 28th International Conference on Machine Learning. 2011: 129-136.
[9] Zhao M, Ohshima H, Tanaka K.Finding “Similar But Different” Documents Based on Coordinate Relationship[C]// Proceedings of the 2016 International Conference on Asian Digital Libraries. 2016: 110-123.
[10] Wang S, Huang M, Deng Z.Densely Connected CNN with Multi-scale Feature Attention for Text Classification[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018: 4468-4474.
[11] Mikolov T, Sutskever I, Chen K, et al.Distributed Representations of Words and Phrases and Their Compositionality[C]// Proceedings of the 2013 Conference on Neural Information Processing Systems, 2013: 3111-3119.
[12] 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. 2014: 1532-1543.
[13] 张庆辉, 万晨霞. 卷积神经网络综述[J]. 中原工学院学报, 2017, 28(3): 82-86.
[13] (Zhang Qinghui, Wan Chenxia.Review of Convolutional Neural Networks[J]. Journal of Zhongyuan University of Technology, 2017, 28(3): 82-86.)
[14] LeCun Y, Bottou L, Bengio Y, et al. Gradient-Based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[15] Krizhevsky A, Sutskever I, Hinton G.ImageNet Classification with Deep Convolutional Neural Networks[C]//Proceedings of the 2012 Conference on Neural Information Processing Systems. 2012: 1097-1105.
[16] Kim Y.Convolutional Neural Networks for Sentence Classification[OL]. arXiv Preprint. arXiv: 1408.5882.
[17] Zhang X, Zhao J, LeCun Y. Character-level Convolutional Networks for Text Classification[C]// Proceedings of the 2015 Conference on Neural Information Processing Systems. 2015: 649-657.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] Guoming Feng,Xiaodong Zhang,Suhui Liu. Classifying Chinese Texts with CapsNet[J]. 数据分析与知识发现, 2018, 2(12): 68-76.
[7] Yanhui Xiao,Xin Wang,Wen’gang Feng,Huawei Tian,Shaozhong Wu,Lihua Li. Predicting Crime Locations Based on Long Short Term Memory and Convolutional Neural Networks[J]. 数据分析与知识发现, 2018, 2(10): 15-20.
[8] Wengang Feng,Jing Huang. Early Warning for Civil Aviation Security Checks Based on Deep Learning[J]. 数据分析与知识发现, 2018, 2(10): 46-53.
[9] Jiaheng Hu,Yonghua Cen,Chengyao Wu. Constructing Sentiment Dictionary with Deep Learning: Case Study of Financial Data[J]. 数据分析与知识发现, 2018, 2(10): 95-102.
[10] Sanhong Deng,Yuyangzi Fu,Hao Wang. Multi-Label Classification of Chinese Books with LSTM Model[J]. 数据分析与知识发现, 2017, 1(7): 52-60.
[11] Danhao Zhu, Lei Yang, Dongbo Wang. Recognizing Chinese Organization Names Based on Deep Learning: A Recurrent Network Model[J]. 数据分析与知识发现, 2016, 32(12): 36-43.
[12] Liyi Zhang,Chang Liu. Combine Deep Belief Networks and Fuzzy Set for Recognition of Fraud Transaction[J]. 现代图书情报技术, 2016, 32(1): 32-39.
[13] Yang Zhimo, Liu Huailiang, Zhao Hui. An Algorithm of Chinese Text Representation Based on Complex Network[J]. 现代图书情报技术, 2014, 30(11): 38-44.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938