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.
(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.)
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.
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.
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.
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.
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.