The Influence of Syntactic Information on Hedge Scope Detection

In this paper we elaborate over the use of sequential supervised learning methods on the task of hedge cue scope detection. We address the task using a learning methodology that proposes the use of an iterative, error-based approach to improve classification performance. We analyze how the incorporation of syntactic constituent information to the learning and post-processing steps produces a performance improvement of almost twelve points in terms of F-score over previously unseen data.