2006 AnEffectiveTwoStageModelforExpl

• (Krishnan & Manning, 2006) ⇒ Vijay Krishnan, and Christopher D. Manning. (2006). “An Effective Two-stage Model for Exploiting Non-local Dependencies in Named Entity Recognition.” In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics. doi:10.3115/1220175.1220316

Subject Headings: Non-local Sequence Tagging Feature; Supervised Sequence Tagging; Global Predictor Feature.

Cited By

• http://scholar.google.com/scholar?q=%22An+effective+two-stage+model+for+exploiting+non-local+dependencies+in+named+entity+recognition%22+2006
• http://dl.acm.org/citation.cfm?id=1220175.1220316&preflayout=flat#citedby

2014

• (Melli, 2014) ⇒ Gabor Melli. (2014). “Shallow Semantic Parsing of Product Offering Titles (for Better Automatic Hyperlink Insertion).” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623343
  • QUOTE: In the future we hope to investigate the work by (Krishnan & Manning, 2006) [7] that uses two phases to introduce global features.

Quotes

Abstract

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-the-art 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 other documents in the test corpus, and this gives us a 13.3% error reduction over NER systems using local-information alone. Additionally, our running time for inference is just the inference time of two sequential CRFs, which is muchthat directly model the dependencies and do approximate inference.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 AnEffectiveTwoStageModelforExpl	Christopher D. Manning Vijay Krishnan			An Effective Two-stage Model for Exploiting Non-local Dependencies in Named Entity Recognition				10.3115/1220175.1220316