A Graph Attention Model for Dictionary-Guided Named Entity Recognition

The lack of human annotations has been one of the main obstacles for neural named entity recognition in low-resource domains. To address this problem, there have been many efforts on automatically generating silver annotations according to domain-specific dictionaries. However, the information of domain dictionaries is usually limited, and the generated annotations may be noisy which poses significant challenges on learning effective models. In this work, we try to alleviate these issues by introducing a dictionary-guided graph attention model. First, domain-specific dictionaries are utilized to extract entity mention candidates by a graph matching algorithm, which can capture word patterns of domain entities. Furthermore, a word-mention interactive graph is leveraged to integrate the semantic and boundary information of entities into their context. We evaluated our model on the biomedical-domain datasets of recognizing chemical and disease entities, namely BC5CDR and NCBI disease corpora. The results show that our model outperforms several state-of-the-art models with different methodologies, such as feature-based models (e.g., BANNER), ensemble models (e.g., CollaboNet), multi-task learning models (e.g., MTM-CW), dictionary-based models (e.g., AutoNER). Moreover, the performance of our model is also comparable with BioBERT that owns huge parameters and needs large-scale pre-training.