UWashington: Negation Resolution using Machine Learning Methods
James Paul White · 2012
This paper reports on a simple system for resolving the scope of negation in the closed track of the *SEM 2012 Shared Task. Cue detection is performed using regular expression rules extracted from the training data. Both scope tokens and negated event tokens are resolved using a Conditional Random Field (CRF) sequence tagger – namely the SimpleTagger library in the MALLET machine learning toolkit. The full negation F1 score obtained for the task evaluation is 48.09 % (P=74.02%, R=35.61%) which ranks this system fourth among the six submitted for the closed track.