The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction

Tommaso Caselli, Piek Vossen


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.
Anthology ID:
W17-2711
Volume:
Proceedings of the Events and Stories in the News Workshop
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
EventStory | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
77–86
Language:
URL:
https://www.aclweb.org/anthology/W17-2711
DOI:
10.18653/v1/W17-2711
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2711.pdf