The Narrative Chains and Event Representation Page


Natural Language Understanding requires a large amount of background "common sense" knowledge about the situation under discussion. In many respects, using this knowledge is at the core of reasoning and acting in traditional Artificial Intelligence. When reading an article about a criminal conviction, the writer assumes the reader knows about trials, juries, and criminal activity. The Narrative Chain project aims to learn this knowledge by processing large amounts of text and learning which events tend to occur together. We are studying not just what can be learned, but also the best representation for this knowledge (graph, linear chain, frame?).

This project also includes research into ordering events in time. For instance, did the conviction or the sentencing happen first? We use modern machine learning techniques to find linguistic features that indicate this semantic ordering relation.

An example of a learned narrative event chain, with arrows indicating temporal ordering, is shown on the right. The bold words are the events, and the subj/obj terms indicate how the common actor in this narrative is involved in the event (the subject or object of the verb).



  • Nathanael Chambers and Dan Jurafsky, "Unsupervised Learning of Narrative Schemas and their Participants". ACL 2009. [pdf]
  • Andrey Gusev, Nathanael Chambers, Pranav Khaitan, Divye Khilnani, Steven Bethard, Dan Jurafsky, "Using Query Patterns to Learn the Duration of Events". IWCS 2011.
  • Nathanael Chambers and Dan Jurafsky, "A Database of Narrative Schemas". LREC 2010. [pdf]
  • Nathanael Chambers and Dan Jurafsky, "Jointly Combining Implicit Constraints Improves Temporal Ordering". EMNLP 2008. [pdf]
  • Nathanael Chambers and Dan Jurafsky, "Unsupervised Learning of Narrative Event Chains". ACL 2008. [pdf]
  • Nathanael Chambers, Shan Wang, and Dan Jurafsky, "Classifying Temporal Relations Between Events". ACL 2007. [pdf]

Contact Information

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