The Event StoryLine Corpus: A new Benchmark for causal and temporal Relation Extraction

T. Caselli; P.T.J.M. Vossen

doi:10.18653/v1/W17-2711

The Event StoryLine Corpus: A new Benchmark for causal and temporal Relation Extraction

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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.

Original language	English
Title of host publication	Proceedings of the workshop Events and Stories in the News
Publisher	ACL Anthology
Pages	77-86
Number of pages	10
ISBN (Electronic)	9781945626630
DOIs	https://doi.org/10.18653/v1/W17-2711
Publication status	Published - 2017

Funding

This work has been supported the NWO Spinoza Prize project “Understanding Language by Machines” (sub-track 3).

Funders	Funder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

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10.18653/v1/W17-2711

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title = "The Event StoryLine Corpus: A new Benchmark for causal and temporal Relation Extraction",

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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doi = "10.18653/v1/W17-2711",

language = "English",

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N2 - 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.

AB - 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.

U2 - 10.18653/v1/W17-2711

DO - 10.18653/v1/W17-2711

M3 - Conference contribution

SP - 77

EP - 86

BT - Proceedings of the workshop Events and Stories in the News

PB - ACL Anthology

ER -

The Event StoryLine Corpus: A new Benchmark for causal and temporal Relation Extraction

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