Extracting Researcher Metadata with Labeled Features

Professional homepages of researchers contain metadata that provides crucial evidence in several digital library tasks such as academic network extraction, record linkage and expertise search. Due to inherent diversity in values for certain metadata fields (e.g., affiliation) supervised algorithms require a large number of labeled examples for accurately identifying values for these fields. We address this issue with feature labeling, a recent semi-supervised machine learning technique. We apply feature labeling to researcher metadata extraction from homepages by combining a small set of expert-provided feature distributions with few fully-labeled examples. We study two types of labeled features: (1) Dictionary features provide unigram hints related to specific metadata fields, whereas, (2) Proximity features capture the layout information between metadata fields on a homepage in a second stage. We experimentally show that this two-stage approach along with labeled features provides significant improvements in the tagging performance. In one experiment with only ten labeled homepages and 22 expert-specified labeled features, we obtained a 45% relative increase in the F1 value for the affiliation field, while the overall F1 improves by 9%.