End-to-End Negation Resolution as Graph Parsing

01/07/2020

End-to-End Negation Resolution as Graph Parsing

Robin Kurtz, Stephan Oepen, Marco Kuhlmann

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Abstract: We present a neural end-to-end architecture for negation resolution based on a formulation of the task as a graph parsing problem. Our approach allows for the straightforward inclusion of many types of graph-structured features without the need for representation-specific heuristics. In our experiments, we specifically gauge the usefulness of syntactic information for negation resolution. Despite the conceptual simplicity of our architecture, we achieve state-of-the-art results on the Conan Doyle benchmark dataset, including a new top result for our best model.

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