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HTML-LSTM: Information Extraction from HTML Tables in Web Pages Using Tree-Structured LSTM

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Discovery Science (DS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12986))

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Abstract

In this paper, we propose a novel method for extracting information from HTML tables with similar contents but with a different structure. We aim to integrate multiple HTML tables into a single table for retrieval of information containing in various Web pages. The method is designed by extending tree-structured LSTM, the neural network for tree-structured data, in order to extract information that is both linguistic and structural information of HTML data. We evaluate the proposed method through experiments using real data published on the WWW.

K. Kawamura—Now at Sony Group Corporation.

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Notes

  1. 1.

    https://mocobeta.github.io/janome/.

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Author information

Authors and Affiliations

  1. Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan

    Kazuki Kawamura & Akihiro Yamamoto

Authors
  1. Kazuki Kawamura
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  2. Akihiro Yamamoto
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Corresponding author

Correspondence to Kazuki Kawamura .

Editor information

Editors and Affiliations

  1. Universidade do Porto and Fraunhofer Portugal AICOS, Porto, Portugal

    Carlos Soares

  2. Dalhousie University, Halifax, NS, Canada

    Luis Torgo

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Kawamura, K., Yamamoto, A. (2021). HTML-LSTM: Information Extraction from HTML Tables in Web Pages Using Tree-Structured LSTM. In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science(), vol 12986. Springer, Cham. https://doi.org/10.1007/978-3-030-88942-5_3

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  • DOI: https://doi.org/10.1007/978-3-030-88942-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88941-8

  • Online ISBN: 978-3-030-88942-5

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