@inproceedings{ma-etal-2023-kepl,
title = "{KEPL}: Knowledge Enhanced Prompt Learning for {C}hinese Hypernym-Hyponym Extraction",
author = "Ma, Ningchen and
Wang, Dong and
Bao, Hongyun and
He, Lei and
Zheng, Suncong",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.358",
doi = "10.18653/v1/2023.emnlp-main.358",
pages = "5858--5867",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction
%A Ma, Ningchen
%A Wang, Dong
%A Bao, Hongyun
%A He, Lei
%A Zheng, Suncong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ma-etal-2023-kepl
%X 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.
%R 10.18653/v1/2023.emnlp-main.358
%U https://aclanthology.org/2023.emnlp-main.358
%U https://doi.org/10.18653/v1/2023.emnlp-main.358
%P 5858-5867
Markdown (Informal)
[KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction](https://aclanthology.org/2023.emnlp-main.358) (Ma et al., EMNLP 2023)
ACL