PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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 pp. 40-47, 2010. ISSN 978-1-932432-84-8

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

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7013
Deposited By:Vincent Van Asch
Deposited On:08 October 2010