UMichigan: A Conditional Random Field Model for Resolving the Scope of Negation

In this paper, we present a system for detecting negation in English text. We address three tasks: negation cue detection, negation scope resolution and negated event identification. We pose these tasks as sequence labeling problems. For each task, we train a Conditional Random Field (CRF) model on lexical, structural, and syntactic features extracted from labeled data. The models are trained and tested using the dataset distributed with the *sem Shared Task 2012 on resolving the scope and focus of negation. The system detects negation cues with 90.98% F1 measure (94.3% and 87.88% recall). It identifies negation scope with 82.70% F1 on token-bytoken level and 64.78% F1 on full scope level. Negated events are detected with 51.10% F1 measure.