Scholarly Document Information Extraction using Extensible Features for Efficient Higher Order Semi-CRFs
Research Area: Digital Libraries Year: 2015
Type of Publication: In Proceedings  
  • Nguyen Viet Cuong
  • Muthu Kumar Chandrasekaran
  • Min-Yen Kan
  • Wee Sun Lee
We address the tasks of recovering bibliographic and document structure metadata from scholarly documents. We leverage higher order semi-Markov conditional random fields to model long-distance label sequences, improving upon the performance of the linear-chain conditional random field model. We introduce the notion of extensible features, which allows the expensive inference process to be simplified through memoization, resulting in lower computational complexity. Our method significantly betters the state-of-the-art on three related scholarly document extraction tasks.
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