A Entity Attention-based model for Entity Relation Classification for Chinese Literature Text
Wenrui Xie · 2021
Entity relation classification is one of the basic tasks in natural language processing. The performance of the existing relational classification in Chinese literature text is not ideal. To address the issues, we propose a entity attention-based model for entity relation classification for Chinese literature text. Our proposed model extracts key information from entity by using attention mechanism, and filters out redundant information. In addition, we integrate entity type information into the model to help the model classify relation more reasonably. Experimental results show that the proposed model outperforms the state-of-the-art methods on Chinese literature text.