Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment
Md Arafat Sultan, Steven J. Bethard, Tamara Sumner · 2015
Monolingual alignment is the task of pairing semantically similar units from two pieces of text.We report a top-performing supervised aligner that operates on short text snippets.We employ a large feature set to ( 1) encode similarities among semantic units (words and named entities) in context, and (2) address cooperation and competition for alignment among units in the same snippet.These features are deployed in a two-stage logistic regression framework for alignment.On two benchmark data sets, our aligner achieves F 1 scores of 92.1% and 88.5%, with statistically significant error reductions of 4.8% and 7.3% over the previous best aligner.It produces top results in extrinsic evaluation as well.