@inproceedings{chen-etal-2021-event,
title = "Event-Centric Natural Language Processing",
author = "Chen, Muhao and
Zhang, Hongming and
Ning, Qiang and
Li, Manling and
Ji, Heng and
McKeown, Kathleen and
Roth, Dan",
editor = "Chiang, David and
Zhang, Min",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-tutorials.2",
doi = "10.18653/v1/2021.acl-tutorials.2",
pages = "6--14",
abstract = "This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text. These include methods to extract the internal structures of an event regarding its protagonist(s), participant(s) and properties, as well as external structures concerning memberships, temporal and causal relations of multiple events. This tutorial will provide audience with a systematic introduction of (i) knowledge representations of events, (ii) various methods for automated extraction, conceptualization and prediction of events and their relations, (iii) induction of event processes and properties, and (iv) a wide range of NLU and commonsense understanding tasks that benefit from aforementioned techniques. We will conclude the tutorial by outlining emerging research problems in this area.",
}
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%0 Conference Proceedings
%T Event-Centric Natural Language Processing
%A Chen, Muhao
%A Zhang, Hongming
%A Ning, Qiang
%A Li, Manling
%A Ji, Heng
%A McKeown, Kathleen
%A Roth, Dan
%Y Chiang, David
%Y Zhang, Min
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F chen-etal-2021-event
%X This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text. These include methods to extract the internal structures of an event regarding its protagonist(s), participant(s) and properties, as well as external structures concerning memberships, temporal and causal relations of multiple events. This tutorial will provide audience with a systematic introduction of (i) knowledge representations of events, (ii) various methods for automated extraction, conceptualization and prediction of events and their relations, (iii) induction of event processes and properties, and (iv) a wide range of NLU and commonsense understanding tasks that benefit from aforementioned techniques. We will conclude the tutorial by outlining emerging research problems in this area.
%R 10.18653/v1/2021.acl-tutorials.2
%U https://aclanthology.org/2021.acl-tutorials.2
%U https://doi.org/10.18653/v1/2021.acl-tutorials.2
%P 6-14
Markdown (Informal)
[Event-Centric Natural Language Processing](https://aclanthology.org/2021.acl-tutorials.2) (Chen et al., ACL-IJCNLP 2021)
ACL
- Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, and Dan Roth. 2021. Event-Centric Natural Language Processing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts, pages 6–14, Online. Association for Computational Linguistics.