A frame-based approach for reference metadata extraction

In this paper, we propose a novel frame-based approach (FBA) and use reference metadata extraction as a case study to demonstrate its advantages. The main contributions of this research are three-fold. First, the new frame matching algorithm, based on sequence alignment, can compensate for the shortcomings of traditional rule-based approach, in which rule matching lacks flexibility and generality. Second, an approximate matching is adopted for capturing reasonable abbreviations or errors in the input reference string to further increase the coverage of the frames. Third, experiments conducted on extensive datasets show that the same knowledge framework performed equally well on various untrained domains. Comparing to a widely-used machine learning method, Conditional Random Fields (CRFs), the FBA can drastically reduce the average field error rate across all four independent test sets by 70% (2.24% vs. 7.54%).