Abstract
In this paper we elaborate over the use of sequential supervised learning methods on the task of hedge cue scope detection. We address the task using a learning methodology that proposes the use of an iterative, error-based approach to improve classification performance. We analyze how the incorporation of syntactic constituent information to the learning and post-processing steps produces a performance improvement of almost twelve points in terms of F-score over previously unseen data.
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Moncecchi, G., Minel, JL., Wonsever, D. (2014). The Influence of Syntactic Information on Hedge Scope Detection. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_7
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DOI: https://doi.org/10.1007/978-3-319-12027-0_7
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