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2006

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

8 years 11 months ago
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...
Vijay Krishnan, Christopher D. Manning
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Vijay Krishnan, Christopher D. Manning
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