Abstract
Modeling hypernym-hyponym (“is-a”) relations is very 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 Architecture to select the matching pattern for the text into prompt, our model embeds patterns and text simultaneously. Additionally, we construct a Chinese hypernym-hyponym relation extraction dataset, which contains three typical scenarios, as baike, news and We-media. The experimental results on the dataset demonstrate the efficiency and effectiveness of our proposed model.- Anthology ID:
- 2023.emnlp-main.358
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5858–5867
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.emnlp-main.358/
- DOI:
- 10.18653/v1/2023.emnlp-main.358
- Cite (ACL):
- Ningchen Ma, Dong Wang, Hongyun Bao, Lei He, and Suncong Zheng. 2023. KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5858–5867, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction (Ma et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.emnlp-main.358.pdf