Chinese Short Text Entity Disambiguation Based on the Dual-Channel Hybrid Network

Entity disambiguation refers to the accurate inference of the real mention of an entity with the same name according to the context. Most existing studies focused on long texts, for short texts, the performance has been unsatisfactory due to sparsity. In this paper, we treat the entity disambiguation task as a classification problem. we propose a novel neural network-based capsule network and convolutional neural network for entity disambiguation, leveraging full semantic information of short text data. In particular, a self-attention mechanism is utilized to further filter the semantic information extracted from the capsule network. On the other hand, a convolutional neural network with combined pooling is established to capture semantics from another channel. In the end, the semantic features obtained by the above models are combined through a fully connected layer to complete the task of entity disambiguation. The experimental results on the CCKS 2019 entity linking dataset showed that the dual-channel hybrid network proposed in this paper achieved an F1-score of 88.04%, which is superior to that of the existing mainstream deep learning model, thereby verifying the effectiveness of the model.