Abstract
Automatic extraction of causal relations from text has the potential to aid in the understanding of complex scenarios, but to date there has been limited work exploring extraction from natural data at scale. We describe a system that implements a rich language processing pipeline for the purpose of extracting causal relations between events described in text. The system uses a syntactic pattern-based approach to causality, using mutual bootstrapping to expand a set of seed patterns by discovering additional high-reliability patterns through a human-in-the-loop approach. We evaluate the performance of the system on newswire data and explore the properties of the causal relations it identifies.