@inproceedings{tran-etal-2020-revisiting,
title = "Revisiting Unsupervised Relation Extraction",
author = "Tran, Thy Thy and
Le, Phong and
Ananiadou, Sophia",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.669",
doi = "10.18653/v1/2020.acl-main.669",
pages = "7498--7505",
abstract = "Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form. However, we demonstrate that by using only named entities to induce relation types, we can outperform existing methods on two popular datasets. We conduct a comparison and evaluation of our findings with other URE techniques, to ascertain the important features in URE. We conclude that entity types provide a strong inductive bias for URE.",
}
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%0 Conference Proceedings
%T Revisiting Unsupervised Relation Extraction
%A Tran, Thy Thy
%A Le, Phong
%A Ananiadou, Sophia
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F tran-etal-2020-revisiting
%X Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form. However, we demonstrate that by using only named entities to induce relation types, we can outperform existing methods on two popular datasets. We conduct a comparison and evaluation of our findings with other URE techniques, to ascertain the important features in URE. We conclude that entity types provide a strong inductive bias for URE.
%R 10.18653/v1/2020.acl-main.669
%U https://aclanthology.org/2020.acl-main.669
%U https://doi.org/10.18653/v1/2020.acl-main.669
%P 7498-7505
Markdown (Informal)
[Revisiting Unsupervised Relation Extraction](https://aclanthology.org/2020.acl-main.669) (Tran et al., ACL 2020)
ACL
- Thy Thy Tran, Phong Le, and Sophia Ananiadou. 2020. Revisiting Unsupervised Relation Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7498–7505, Online. Association for Computational Linguistics.