Corpus-Aware Graph Aggregation Network for Sequence Labeling

Current state-of-the-art sequence labeling models are typically based on sequential architecture such as Bi-directional LSTM (BiLSTM). However, the structure of processing a word at a time based on the sequential order restricts the full utilization of non-sequential features, including syntactic relationships, word co-occurrence relations, and document topics. They can be regarded as the corpus-level features and critical for sequence labeling. In this paper, we propose a Corpus-Aware Graph Aggregation Network. Specifically, we build three types of graphs, i.e., a word-topic graph, a word co-occurrence graph, and a word syntactic dependency graph, to express different kinds of corpus-level non-sequential features. After that, a graph convolutional network (GCN) is adapted to model the relations between words and non-sequential features. Finally, we employ a label-aware attention mechanism to aggregate corpus-aware non-sequential features and sequential ones for sequence labeling. The experimental results on four sequence labeling tasks (named entity recognition, chunking, multilingual sequence labeling, and target-based sentiment analysis) show that our model achieves state-of-the-art performance.