Roser Morante
ABSTRACT
Finding negation signals and their scope in text is an important subtask in information extraction. In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system combines several classifiers and works in two phases. To investigate the robustness of the approach, the system is tested on the three subcorpora of the BioScope corpus representing different text types. It achieves the best results to date for this task, with an error reduction of 32.07% compared to current state of the art results.
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Index Terms
- A metalearning approach to processing the scope of negation
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IWCS '11: Proceedings of the Ninth International Conference on Computational SemanticsIn this paper we summarize existing work on the recently introduced task of processing the scope of negation and modality cues; we analyse the scope model that existing systems can process, which is mainly the model reflected in the annotations of the ...
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