@inproceedings{yao-etal-2020-annotating,
title = "{A}nnotating {T}emporal {D}ependency {G}raphs via {C}rowdsourcing",
author = "Yao, Jiarui and
Qiu, Haoling and
Min, Bonan and
Xue, Nianwen",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.432",
doi = "10.18653/v1/2020.emnlp-main.432",
pages = "5368--5380",
abstract = "We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text. We argue that temporal dependency graphs, built on previous research on narrative times and temporal anaphora, provide a representation scheme that achieves a good trade-off between completeness and practicality in temporal annotation. We also provide a crowdsourcing strategy to annotate TDGs, and demonstrate the feasibility of this approach with an evaluation of the quality of the annotation, and the utility of the resulting data set by training a machine learning model on this data set. The data set is publicly available.",
}
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<abstract>We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text. We argue that temporal dependency graphs, built on previous research on narrative times and temporal anaphora, provide a representation scheme that achieves a good trade-off between completeness and practicality in temporal annotation. We also provide a crowdsourcing strategy to annotate TDGs, and demonstrate the feasibility of this approach with an evaluation of the quality of the annotation, and the utility of the resulting data set by training a machine learning model on this data set. The data set is publicly available.</abstract>
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%0 Conference Proceedings
%T Annotating Temporal Dependency Graphs via Crowdsourcing
%A Yao, Jiarui
%A Qiu, Haoling
%A Min, Bonan
%A Xue, Nianwen
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yao-etal-2020-annotating
%X We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text. We argue that temporal dependency graphs, built on previous research on narrative times and temporal anaphora, provide a representation scheme that achieves a good trade-off between completeness and practicality in temporal annotation. We also provide a crowdsourcing strategy to annotate TDGs, and demonstrate the feasibility of this approach with an evaluation of the quality of the annotation, and the utility of the resulting data set by training a machine learning model on this data set. The data set is publicly available.
%R 10.18653/v1/2020.emnlp-main.432
%U https://aclanthology.org/2020.emnlp-main.432
%U https://doi.org/10.18653/v1/2020.emnlp-main.432
%P 5368-5380
Markdown (Informal)
[Annotating Temporal Dependency Graphs via Crowdsourcing](https://aclanthology.org/2020.emnlp-main.432) (Yao et al., EMNLP 2020)
ACL
- Jiarui Yao, Haoling Qiu, Bonan Min, and Nianwen Xue. 2020. Annotating Temporal Dependency Graphs via Crowdsourcing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5368–5380, Online. Association for Computational Linguistics.