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Table Orientation Classification Model Based on BERT and TCSMN

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Intelligent Information Processing XII (IIP 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 703))

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Abstract

Tables are commonly used for structuring and consolidating knowledge, significantly enhancing the efficiency for human readers to acquire relevant information. However, due to their diverse structures and open domains, employing computational methods for their automatic analysis remains a substantial challenge. Among these challenges, accurately classifying the forms of tables is fundamental for achieving deep comprehension and analysis, forming the basis for understanding, retrieving, and extracting knowledge within tables. Common table formats include row tables, column tables, and matrix tables, where data is arranged in rows, columns, and combinations of rows and columns, respectively. This paper introduces a novel approach for table classification based on the neural network model, TableTC. TableTC initially utilizes fine-tuning of the BERT pre-trained model to comprehend table content. Additionally, it proposes an improved Temporal Convolutional Network (TCN) named Temporal Convolutional Sparse Multilayer Perceptron Network (TCSMN). This network captures sequential structural features of cells and their surrounding neighbors, enhancing the ability to extract semantic features and positions. Finally, it employs an attention mechanism to further augment the capability of extracting row-column positions and semantic features. The evaluation of our proposed method is conducted using table data from scientific literature found in the PubMed Central website. Experimental results demonstrate that TableTC achieves a 2.7% improvement in table classification accuracy, as measured by the F1 score, compared to previous state-of-the-art methods on this dataset.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2022YFC3302300), Advanced Research Project (7090201050307), National 242 Information Security Program(2022A056, 2023A105).

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Authors and Affiliations

  1. Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450003, China

    Dawei Jin & Tianhang Song

  2. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology (CAS), Beijing, 100190, China

    Dawei Jin & Tianhang Song

  3. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China

    Rongxin Mi

Authors
  1. Dawei Jin
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  2. Rongxin Mi
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  3. Tianhang Song
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Corresponding author

Correspondence to Rongxin Mi .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Oslo, Oslo, Norway

    Jim Torresen

  3. De Montfort University, Leicester, UK

    Shengxiang Yang

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Jin, D., Mi, R., Song, T. (2024). Table Orientation Classification Model Based on BERT and TCSMN. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_4

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  • DOI: https://doi.org/10.1007/978-3-031-57808-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57807-6

  • Online ISBN: 978-3-031-57808-3

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