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Neural Segmental Hypergraphs for Overlapping Mention Recognition

2018-10-03EMNLP 2018Code Available0· sign in to hype

Bailin Wang, Wei Lu

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Abstract

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

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
ACE 2004Neural segmental hypergraphsF175.1Unverified
ACE 2005Neural segmental hypergraphsF174.5Unverified
GENIANeural segmental hypergraphsF175.1Unverified

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