Chinese Abbreviation Prediction Using Multi-Feature Fusion and Global Context

Chinese abbreviation prediction is essential for various natural language processing tasks, including query comprehension, entity linking, and information retrieval.Existing approaches rely on sequence tagging for abbreviation prediction.However, these approaches fail to guarantee the predicted abbreviations preserve their respective meanings and only consider context information related to the entity itself.This paper proposes a novel Chinese abbreviation prediction approach using multi-feature fusion and global context.The approach initially generates multiple candidate abbreviations using a Chinese pretrained unbalanced Transformer generative model.It then selects high-quality candidates through a multi-feature optimization stage, and finally evaluates their quality within the global contextual information.Experimental results indicate that our approach surpasses other comparable approaches in abbreviation prediction.