@inproceedings{han-etal-2020-data,
title = "More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction",
author = "Han, Xu and
Gao, Tianyu and
Lin, Yankai and
Peng, Hao and
Yang, Yaoliang and
Xiao, Chaojun and
Liu, Zhiyuan and
Li, Peng and
Zhou, Jie and
Sun, Maosong",
editor = "Wong, Kam-Fai and
Knight, Kevin and
Wu, Hua",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-main.75",
pages = "745--758",
abstract = "Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require {``}more{''} from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.",
}
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<abstract>Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require “more” from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.</abstract>
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%0 Conference Proceedings
%T More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
%A Han, Xu
%A Gao, Tianyu
%A Lin, Yankai
%A Peng, Hao
%A Yang, Yaoliang
%A Xiao, Chaojun
%A Liu, Zhiyuan
%A Li, Peng
%A Zhou, Jie
%A Sun, Maosong
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F han-etal-2020-data
%X Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require “more” from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.
%U https://aclanthology.org/2020.aacl-main.75
%P 745-758
Markdown (Informal)
[More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction](https://aclanthology.org/2020.aacl-main.75) (Han et al., AACL 2020)
ACL
- Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, and Maosong Sun. 2020. More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 745–758, Suzhou, China. Association for Computational Linguistics.