Recognition of Nested Entity with Dependency Information

Named entity recognition (NER) is a basic task in natural language processing. However, most existing models are hard to detect entities with nested structure which means that an entity contains one or more entities. In this paper, we propose a boundary-aware approach for nested NER. First, word information is incorporated in the same dimension via Lexicon, in which characters are feed into LSTM to learn internal structure of words and obtain character representation. To augment word representation, Graph Convolutional Network (GCN) is applied to extract dependency information between entities. Second, our model can detect boundaries to locate entity by using Star-Transformer, which is suitable for small-scale corpus and unstructured texts because of its star structure. Based on predicted boundaries, our model utilizes boundary-aware regions to predict entity categorical labels, which can reduce the number of candidate entities and decrease computation cost. In our experiment, it shows an impressive improvement on forum corpus and that our model can perform well on a small-scale corpus.