Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
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NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
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
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Prototype-driven learning for sequence models
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
On multi-class cost-sensitive learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Modeling latent-dynamic in shallow parsing: a latent conditional model with improved inference
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Semi-supervised training for the averaged perceptron POS tagger
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A simple unsupervised learner for POS disambiguation rules given only a minimal lexicon
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Painless unsupervised learning with features
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Efficient graph-based semi-supervised learning of structured tagging models
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Part-of-speech tagging from 97% to 100%: is it time for some linguistics?
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Unsupervised part-of-speech tagging with bilingual graph-based projections
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Semisupervised condensed nearest neighbor for part-of-speech tagging
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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All types of part-of-speech (POS) tagging errors have been equally treated by existing taggers. However, the errors are not equally important, since some errors affect the performance of subsequent natural language processing (NLP) tasks seriously while others do not. This paper aims to minimize these serious errors while retaining the overall performance of POS tagging. Two gradient loss functions are proposed to reflect the different types of errors. They are designed to assign a larger cost to serious errors and a smaller one to minor errors. Through a set of POS tagging experiments, it is shown that the classifier trained with the proposed loss functions reduces serious errors compared to state-of-the-art POS taggers. In addition, the experimental result on text chunking shows that fewer serious errors help to improve the performance of subsequent NLP tasks.