用树形LSTM提取网页表格的语义与结构信息，统一不同格式数据。
HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM
- 用树形LSTM建模HTML表格的层级结构和文本内容
- 在真实网页数据上实现跨表信息整合，提升抽取一致性
- 适合处理结构差异大但内容相似的网页表格
本文提出一种新方法，用于从内容相似但结构不同的网页HTML表格中提取信息。目标是将多个不同结构的HTML表格整合为单一结构，便于跨页面信息检索。该方法通过扩展用于树形结构数据的树形LSTM神经网络，同时捕捉HTML数据的语言特征与结构特征。实验基于万维网实际发布数据进行验证，结果表明该方法能有效应对表格结构多样性问题，提升信息抽取的准确性和泛化能力。
原文摘要 · Abstract (English)
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.
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