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In this work, we propose a novel segmental hypergraph representation to modeloverlapping entity mentions that are prevalent in many practical datasets. Weshow that our model built on top of such a new representation is able tocapture features and interactions that cannot be captured by previous modelswhile maintaining a low time complexity for inference. We also present atheoretical analysis to formally assess how our representation is better thanalternative representations reported in the literature in terms ofrepresentational power. Coupled with neural networks for feature learning, ourmodel achieves the state-of-the-art performance in three benchmark datasetsannotated with overlapping mentions.

Neural Segmental Hypergraphs for Overlapping Mention Recognition