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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.19445 |
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| _version_ | 1866917789446438912 |
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| author | Kawamura, Kazuki Yamamoto, Akihiro |
| author_facet | Kawamura, Kazuki Yamamoto, Akihiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19445 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM Kawamura, Kazuki Yamamoto, Akihiro Information Retrieval Machine Learning 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. |
| title | HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2409.19445 |