@inproceedings{caselli-vossen-2017-event,
title = "The Event {S}tory{L}ine Corpus: A New Benchmark for Causal and Temporal Relation Extraction",
author = "Caselli, Tommaso and
Vossen, Piek",
editor = "Caselli, Tommaso and
Miller, Ben and
van Erp, Marieke and
Vossen, Piek and
Palmer, Martha and
Hovy, Eduard and
Mitamura, Teruko and
Caswell, David",
booktitle = "Proceedings of the Events and Stories in the News Workshop",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2711",
doi = "10.18653/v1/W17-2711",
pages = "77--86",
abstract = "This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems.",
}
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<abstract>This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems.</abstract>
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%0 Conference Proceedings
%T The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction
%A Caselli, Tommaso
%A Vossen, Piek
%Y Caselli, Tommaso
%Y Miller, Ben
%Y van Erp, Marieke
%Y Vossen, Piek
%Y Palmer, Martha
%Y Hovy, Eduard
%Y Mitamura, Teruko
%Y Caswell, David
%S Proceedings of the Events and Stories in the News Workshop
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F caselli-vossen-2017-event
%X This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems.
%R 10.18653/v1/W17-2711
%U https://aclanthology.org/W17-2711
%U https://doi.org/10.18653/v1/W17-2711
%P 77-86
Markdown (Informal)
[The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction](https://aclanthology.org/W17-2711) (Caselli & Vossen, EventStory 2017)
ACL