Syntactic Scope Resolution in Uncertainty Analysis

Abstract

We show how the use of syntactic structure enables the resolution of hedge scope in a hybrid, two-stage approach to uncertainty analysis. In the first stage, a Maximum Entropy classifier, combining surface-oriented and syntactic features, identifies cue words. With a small set of hand-crafted rules operating over dependency representations in stage two, we attain the best overall result (in terms of both combined ranks and average F1) in the 2010 CoNLL Shared Task.