Contingent Pairs of Fine-Grained Everyday Events

If you use this data in your research, please refer to and cite: 

Elahe Rahimtoroghi, Ernesto Hernandes and Marilyn A Walker, "Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events", Proceedings of 17th Annual SIGDial Meeting on Discourse and Dialouge (SIGDial 2016), Los Angeles, USA, 2016. (Best Paper Award)


Overview: Much of the user-generated content on social media is provided by ordinary people telling stories about their daily lives.  We develop and test a novel method for learning fine-grained common-sense knowledge from these stories about contingent (causal and conditional) relationships between everyday events.  This type of knowledge is useful for text and story understanding, information extraction, question answering, and text summarization. We test and compare different methods for learning contingency relation, and compare what is learned from topic-sorted story collections vs. general-domain stories.  

The Data: This dataset contains contingent pairs of everyday events, with Causal Potential and Frequency scores, extracted from our corpus of topic-specific personal blog posts. An evaluation on Amazon Mechanical Turk shows 82% of the relations between events that we learn from topic-sorted stories are judged as contingent. 

Related Papers:

Hu, Zhichao, Elahe Rahimtoroghi, Larissa Munishkina, Reid Swanson, and Marilyn A. Walker. "Unsupervised Induction of Contingent Event Pairs from Film Scenes." In Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, Washington, USA, 2013.


Download: Fill out the following form to download Contingent Pairs of Fine-Grained Everyday Events Corpus.

Contact: Please direct questions to Elahe Rahimtoroghi: elahe [at] soe [dot] ucsc [dot] edu

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