Resolving Speculation and Negation Scope in Biomedical Articles with a Syntactic Constituent Ranker
نویسندگان
چکیده
We discuss how the scope of speculation and negation can be resolved by learning a ranking function that operates over syntactic constituent subtrees. An important assumption of this method is that scope aligns with constituents, and hence we investigate instances of disalignment. We also show how the method can be combined with an existing scope-resolution system based on manually-crafted rules over dependency structures. While both systems achieve encouraging results, combining the two improves performance beyond either in isolation. Furthermore, coupling this hybrid scope approach with an SVM cue classifier achieves the best published results on data from the CoNLL-2010 Shared Task.
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