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
[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.
裴晶晶,乐小虬. 篇章级并列关系文本块识别方法研究[J]. 数据分析与知识发现, 2019, 3(5): 51-56.
Jingjing Pei,Xiaoqiu Le. Identifying Coordinate Text Blocks in Discourses. Data Analysis and Knowledge Discovery, 2019, 3(5): 51-56.
(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.
(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.)
(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.)
(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.
(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.