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ACL
2006

Training Conditional Random Fields with Multivariate Evaluation Measures

14 years 1 months ago
Training Conditional Random Fields with Multivariate Evaluation Measures
This paper proposes a framework for training Conditional Random Fields (CRFs) to optimize multivariate evaluation measures, including non-linear measures such as F-score. Our proposed framework is derived from an error minimization approach that provides a simple solution for directly optimizing any evaluation measure. Specifically focusing on sequential segmentation tasks, i.e. text chunking and named entity recognition, we introduce a loss function that closely reflects the target evaluation measure for these tasks, namely, segmentation F-score. Our experiments show that our method performs better than standard CRF training.
Jun Suzuki, Erik McDermott, Hideki Isozaki
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Jun Suzuki, Erik McDermott, Hideki Isozaki
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