Neural Segmental Hypergraphs for Overlapping Mention Recognition

发表时间2018年  

使用超图结构 解决重叠实体识别问题

Abstract:

        In this work, we propose a novel segmen tal 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. Contributions:  1. 提出来 segmental hypergraph 来构建给定句子中任意的组合。模型时间复杂度是 O(c m n)，能够捕获到 其他模型无法获取的特征； 超图结构：

 Related Work

  1.1 Overlapping Mention Recognition 重叠介绍

  1.2 Neural Model for Mention Recognition 神经模型介绍

  1.3 Segmental Hypergraph 节段超图介绍

          

   1.4 Theoretical Analysis 理论分析

论文代码链接：http://statnlp.org/research/ie

 

标签：Neural,Overlapping,Mention,超图,representation,our,model,Recognition
来源： https://blog.csdn.net/weixin_51418487/article/details/120909738