Finding Cycles in Graph: A Unified Approach for Various NER Tasks

Named Entity Recognition (NER) is the task of recognizing the entities' locations and types in text, which can be generally categorized into flat NER, overlapped NER, and discontinuous NER. Most previous methods are usually designed specifically for one of the tasks, such as sequence labeling approaches for flat NER and span-based models for overlapped NER. Recently, some new work has begun to propose the unified NER framework that can addresses all three scenarios simultaneously. However, there still has room for improvement in some complex scenarios (long/discontinuous entity). In this paper, we propose a concise framework that supports all types of NER tasks, where entities can be represented by unique cycles that are formed by the directed edges among tokens in the graph. The model integrates a Graph Feature Enhancement module to extract correlations at both the node-level and edge-level. At the node-level, the features are enhanced in the binary and ternary token relations. In edge-level, the model will go further to enhance the relations among token pairs using deformable convolutions. Furthermore, to benefit the completeness of cycle formation, we also propose a novel Cycle Loss that optimizes the independent edge classification in the group of cycles from a global perspective. Experimental results show that our model can achieve competitive and even new state-of-the-art performance on eight popular NER benchmarks, including flat NER, overlapped NER, and discontinuous NER.