AERNs: Attention-based entity region networks for multi-grained named entity recognition

Sequential labeling-based named entity recognition approaches restrict each word belonging to at most one entity region, which will have a problem when recognizing the nested named entities. Various models for nested named entity recognition are designed explicitly and usually do not perform well on non-overlapping named entity recognition compared to sequence labeling models. To tackle the aforementioned problem, we propose a novel model named Attention-Based Entity Region Networks (AERNs) for multi-grained entity recognition where multiple entities in a sentence could be non-overlapping or nested. AERNs consist of an entity region recognizer that examines all possible entity regions and an entity region classifier for classifying the regions. Context information is incorporated to improve the performance of named entity recognition by using an attention mechanism that helps the model focus on entity-related context information. Experiments show that AERNs can effectively recognize both nested and non-overlapping entities, and improves the state-of-the-art result by around 3% on several benchmarks datasets.