On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Alternate Objective Function for Markovian Fields
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A maximal figure-of-merit learning approach to text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Investigating loss functions and optimization methods for discriminative learning of label sequences
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Maximum expected F-measure training of logistic regression models
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Minimum tag error for discriminative training of conditional random fields
Information Sciences: an International Journal
Discriminative word alignment via alignment matrix modeling
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Tag confidence measure for semi-automatically updating named entity recognition
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Softmax-margin CRFs: training log-linear models with cost functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Optimizing informativeness and readability for sentiment summarization
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Evaluating information extraction
CLEF'10 Proceedings of the 2010 international conference on Multilingual and multimodal information access evaluation: cross-language evaluation forum
IWSDS'10 Proceedings of the Second international conference on Spoken dialogue systems for ambient environments
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Entity set expansion using topic information
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Minimum-risk training of approximate CRF-based NLP systems
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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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.