An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition

This paper shows that a simple two-stage approach to handle non-local dependencies in Named Entity Recognition (NER) can outperform existing approaches that handle non-local dependencies, while being much more computationally efficient. NER systems typically use sequence models for tractable inference, but this makes them unable to capture the long distance structure present in text. We use a Conditional Random Field (CRF) based NER system using local features to make predictions and then train another CRF which uses both local information and features extracted from the output of the first CRF. Using features capturing non-local dependencies from the same document, our approach yields a 12.6% relative error reduction on the F1 score, over state-of-theart NER systems using local-information alone, when compared to the 9.3% relative error reduction offered by the best systems that exploit non-local information. Our approach also makes it easy to incorporate non-local information from o...

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Vijay Krishnan, Christopher D. Manning
• Keywords: ACL 2006, ACL 2007, Error Reduction, NER Systems, Non-local Dependencies