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
DOIs	https://doi.org/10.18653/v1/W17-2711
Publication status	Published - 2017

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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.",

author = "T. Caselli and P.T.J.M. Vossen",

year = "2017",

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

language = "English",

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booktitle = "Proceedings of the workshop Events and Stories in the News",

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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.

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

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