A Comprehensive Gold Standard for the Enron Organizational Hierarchy

Many researchers have attempted to predict the Enron corporate hierarchy from the data. This work, however, has been hampered by a lack of data. We present a new, large, and freely available gold-standard hierarchy. Using our new gold standard, we show that a simple lower bound for social network-based systems outperforms an upper bound on the approach taken by current NLP systems.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Apoorv Agarwal, Adinoyi Omuya, Aaron Harnly, Owen Rambow
• Keywords: ACL 2012, Computational Linguistics, Corporate Hierarchy, Lower Bound, NLP Systems