Sentence Hedge Detection without Cue Annotation: A Heuristic Cue Selection Approach

Abstract

This paper presents a simple yet effective approach to sentence-level uncertainty detection which does not require cue word annotation. Unlike previous works, the proposed method focuses on cue selection, decoupling it from disambiguation and by optimizing it over sentence hedging error rate. High performance for the task is achieved in experiments, even for settings with poor disambiguation, without cue annotation and with otherwise unreliable corpora from a machine learning point-of-view.