UMichigan: A Conditional Random Field Model for Resolving the Scope of Negation
Published 7 June 2012
Amjad Abu Jbara, Dragomir Radev
Citations14
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TL;DR
A system for detecting negation in English text using a Conditional Random Field model on lexical, structural, and syntactic features extracted from labeled data for three tasks: negation cue detection, negation scope resolution and negated event identification.
Abstract
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.
Keywords
Computer Science
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