KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction

Abstract

Modeling hypernym-hyponym ("is-a") relations is important for many natural language processing (NLP) tasks, such as classification, natural language inference and relation extraction. Existing work on is-a relation extraction is mostly in the English language environment. Due to the flexibility of language expression and the lack of high-quality Chinese annotation datasets, it is still a challenge to accurately identify such relations from Chinese unstructured texts. To tackle this problem, we propose a Knowledge Enhanced Prompt Learning (KEPL) method for Chinese hypernym-hyponym relation extraction. Our model uses the Hearst-like patterns as the prior knowledge. By exploiting a Dynamic Adaptor to select the matching pattern for the text into the prompt, our method simultaneously embedding patterns and text. Additionally, we construct a Chinese hypernym-hyponym relation extraction dataset, which contains three typical scenarios, as Baidu Encyclopedia, news and We-media. The experimental results on the dataset demonstrate the efficiency and effectiveness of our proposed model.