@inproceedings{447b427d0c214d139dd27cf908262138,
title = "MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering",
abstract = "Relation clustering is a general approach for open relation extraction (OpenRE). Current methods have two major problems. One is that their good performance relies on large amounts of labeled and pre-defined relational instances for pre-training, which are costly to acquire in reality. The other is that they only focus on learning a high-dimensional metric space to measure the similarity of novel relations and ignore the specific relational representations of clusters. In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few predefined relational instances as well as mine the specific meanings for cluster interpretability. To our best knowledge, we are the first to introduce a prompt-based framework for unlabeled clustering. Experimental results on different datasets show that MatchPrompt achieves the new SOTA results for OpenRE.",
author = "Jiaxin Wang and Lingling Zhang and Jun Liu and Xi Liang and Yujie Zhong and Yaqiang Wu",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 ; Conference date: 07-12-2022 Through 11-12-2022",
year = "2022",
doi = "10.18653/v1/2022.emnlp-main.537",
language = "英语",
series = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022",
publisher = "Association for Computational Linguistics (ACL)",
pages = "7875--7888",
editor = "Yoav Goldberg and Zornitsa Kozareva and Yue Zhang",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022",
}