Error Analysis in an Automated Narrative Information Extraction Pipeline

Abstract

In this paper, we present our method for automatically extracting narrative information of characters and their narrative roles from natural language stories. In our corpus of 15 unannotated folk tales, our Voz system identifies 87% of the characters in the stories and correctly assigns 68% of the character roles. To better understand the sources of error in our system, we present an analytical methodology to study how the error is introduced by different modules and how it propagates through the pipeline. This methodology allows us to identify the bottleneck with the largest impact on the final error, which might be different from the module with the largest individual error in isolation. Our methodology can be applied to a wide variety of similar information extraction pipelines.