@inproceedings{su-etal-2018-global,
title = "Global Relation Embedding for Relation Extraction",
author = {Su, Yu and
Liu, Honglei and
Yavuz, Semih and
G{\"u}r, Izzeddin and
Sun, Huan and
Yan, Xifeng},
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1075",
doi = "10.18653/v1/N18-1075",
pages = "820--830",
abstract = "We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9{\%} to 89.3{\%}.",
}
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<abstract>We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9% to 89.3%.</abstract>
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%0 Conference Proceedings
%T Global Relation Embedding for Relation Extraction
%A Su, Yu
%A Liu, Honglei
%A Yavuz, Semih
%A Gür, Izzeddin
%A Sun, Huan
%A Yan, Xifeng
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F su-etal-2018-global
%X We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9% to 89.3%.
%R 10.18653/v1/N18-1075
%U https://aclanthology.org/N18-1075
%U https://doi.org/10.18653/v1/N18-1075
%P 820-830
Markdown (Informal)
[Global Relation Embedding for Relation Extraction](https://aclanthology.org/N18-1075) (Su et al., NAACL 2018)
ACL
- Yu Su, Honglei Liu, Semih Yavuz, Izzeddin Gür, Huan Sun, and Xifeng Yan. 2018. Global Relation Embedding for Relation Extraction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 820–830, New Orleans, Louisiana. Association for Computational Linguistics.