@inproceedings{hu-etal-2017-inference,
title = "Inference of Fine-Grained Event Causality from Blogs and Films",
author = "Hu, Zhichao and
Rahimtoroghi, Elahe and
Walker, Marilyn",
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/W17-2708/",
doi = "10.18653/v1/W17-2708",
pages = "52--58",
abstract = "Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80{\%} of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire."
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%0 Conference Proceedings
%T Inference of Fine-Grained Event Causality from Blogs and Films
%A Hu, Zhichao
%A Rahimtoroghi, Elahe
%A Walker, Marilyn
%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 hu-etal-2017-inference
%X Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80% of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire.
%R 10.18653/v1/W17-2708
%U https://aclanthology.org/W17-2708/
%U https://doi.org/10.18653/v1/W17-2708
%P 52-58
Markdown (Informal)
[Inference of Fine-Grained Event Causality from Blogs and Films](https://aclanthology.org/W17-2708/) (Hu et al., EventStory 2017)
ACL