Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/W17-2708 | NEWS@ACL |
DocType | Volume | ISSN |
Conference | abs/1708.09453 | Events and Stories in the News Workshop, ACL 2017 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhichao Hu | 1 | 11 | 1.97 |
| Elahe Rahimtoroghi | 2 | 12 | 2.64 |
| Marilyn A Walker | 3 | 3893 | 418.91 |