论文标题
嵌套命名实体识别的双分式扁平网络
Bipartite Flat-Graph Network for Nested Named Entity Recognition
论文作者
论文摘要
在本文中，我们为嵌套的命名实体识别（NER）提出了一个新颖的二分式扁平网络（BIFLAG），该网络包含两个子图模块：最外层实体的平坦NER模块和一个位于内层中所有实体的图形模块。双向LSTM（BILSTM）和图形卷积网络（GCN）被采用以共同学习扁平实体及其内部依赖性。与以前的模型不同，该模型仅考虑从最内向层到外层（或外部）的单向传递，我们的模型有效地捕获了它们之间的双向相互作用。我们首先使用Flat Ner模块识别的实体来构建实体图，该图形被馈送到下一个图形模块。从图形模块中学到的更丰富的表示具有内部实体的依赖性，可以利用以改善最外面的实体预测。三个标准嵌套数据集的实验结果表明，我们的BIFLAG的表现优于先前的最先进模型。
In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (GCN) are adopted to jointly learn flat entities and their inner dependencies. Different from previous models, which only consider the unidirectional delivery of information from innermost layers to outer ones (or outside-to-inside), our model effectively captures the bidirectional interaction between them. We first use the entities recognized by the flat NER module to construct an entity graph, which is fed to the next graph module. The richer representation learned from graph module carries the dependencies of inner entities and can be exploited to improve outermost entity predictions. Experimental results on three standard nested NER datasets demonstrate that our BiFlaG outperforms previous state-of-the-art models.
