- (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
- (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
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.
|2006 AnEffectiveTwoStageModelforExpl||Vijay Krishnan|
Christopher D. Manning
|An Effective Two-stage Model for Exploiting Non-local Dependencies in Named Entity Recognition||10.3115/1220175.1220316||2006|