HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM
This addresses the challenge of information retrieval from diverse web tables for users needing consolidated data, but it appears incremental as it extends an existing method.
The paper tackles the problem of extracting information from HTML tables with varying structures by integrating multiple tables into a single one, achieving results evaluated on real web data.
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.