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. Our experiments show that using topicspecific datasets enables learning finergrained knowledge about events and results in significant improvement over the baselines. An evaluation on Amazon Mechanical Turk shows 82% of the relations between events that we learn from topic-sorted stories are judged as contingent.
REF learned contingency relations between events from a corpus of blog posts.
Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events
17th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2016)
Computer Science