Memory-based resolution of in-sentence scopes of hedge cues
Roser Morante, Vincent Van Asch and Walter Daelemans
In: Fourteenth Conference on Computational Natural Language Learning: Shared Task, 15-16 July 2010, Uppsala, Sweden.

Abstract

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 memory-based system that relies on information from syntactic dependencies. This system scored the highest F1 (57.32) of Task 2.

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