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Extracting structured data from HTML documents is a long-studied problem witha broad range of applications like augmenting knowledge bases, supportingfaceted search, and providing domain-specific experiences for key verticalslike shopping and movies. Previous approaches have either required a smallnumber of examples for each target site or relied on carefully handcraftedheuristics built over visual renderings of websites. In this paper, we presenta novel two-stage neural approach, named FreeDOM, which overcomes both theselimitations. The first stage learns a representation for each DOM node in thepage by combining both the text and markup information. The second stagecaptures longer range distance and semantic relatedness using a relationalneural network. By combining these stages, FreeDOM is able to generalize tounseen sites after training on a small number of seed sites from that verticalwithout requiring expensive hand-crafted features over visual renderings of thepage. Through experiments on a public dataset with 8 different verticals, weshow that FreeDOM beats the previous state of the art by nearly 3.7 F1 pointson average without requiring features over rendered pages or expensivehand-crafted features.

FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents