Memory-based resolution of in-sentence scopes of hedge cues
Roser Morante, Vincent Van Asch and Walter Daelemans
Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL): Shared Task
In this paper we describe the machine learning systems that we submitted to the CoNLL-2010 Shared Task on Learning to Detect Hedges and Their Scope in Natural Language Text. Task 1 on detecting
uncertain information was performed by an SVM-based system to process the Wikipedia data and by a memory-based system to process the biological data. Task 2 on resolving in-sentence scopes of hedge cues, was performed by a memorybased system that relies on information from syntactic dependencies. This system scored the highest F1 (57.32) of Task 2.