Entity linking for Chinese short texts based on multi-task learning
Zhongge Liu, Minghui Yu · 2023
Entity linking task aims to establish unambiguous links between entity mentions in text and target entities in the knowledge base. Traditional entity linking models rely on complete context information in long texts. However, with the fast-growing amount of internet data, most of the data is in short text which presents a range of challenges including poor contextual semantics and disordered word order. Additionally, compared to English, Chinese poses greater challenges for entity linking task due to its unique language characteristics. To address this, we propose a multi-task learning based entity linking model for Chinese short text. By utilizing the fine-grained entity typing task as an auxiliary task for entity linking, and sharing the BERT-BiLSTM encoder, the model can leverage the implicit relationship between the two tasks to maximize potential information in the text and enhance performance in entity linking. Experimental results on Chinese short text datasets demonstrate that our proposed model outperforms a range of deep-learning based entity linking models, as well as the single-task entity linking model with the same structure, fully verifying the effectiveness of the proposed approach.