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
- Venue:
- EventStory
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 77–86
- Language:
- URL:
- https://aclanthology.org/W17-2711
- DOI:
- 10.18653/v1/W17-2711
- Cite (ACL):
- Tommaso Caselli and Piek Vossen. 2017. The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction. In Proceedings of the Events and Stories in the News Workshop, pages 77–86, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction (Caselli & Vossen, EventStory 2017)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/W17-2711.pdf
- Data
- ECB+