Error reduction through learning multiple descriptions
Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Combining Classifiers for word sense disambiguation
Natural Language Engineering
Analyses for elucidating current question answering technology
Natural Language Engineering
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Improving data driven wordclass tagging by system combination
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Estimating upper and lower bounds on the performance of word-sense disambiguation programs
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Combining heterogeneous classifiers for word-sense disambiguation
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Application of stacked methods to part-of-speech tagging of polish
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I
Developing a competitive HMM arabic POS tagger using small training corpora
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
The UPF learner translation corpus as a resource for translator training
Language Resources and Evaluation
| Hi-index | 0.00 |
In this paper, we attempt to make a formal analysis of the performance in automatic part of speech tagging. Lower and upper bounds in tagging precision using existing taggers or their combination are provided. Since we show that with existing taggers, automatic perfect tagging is not possible, we offer two solutions for applications requiring very high precision: (1) a solution involving minimum human intervention for a precision of over 98.7%, and (2) a combination of taggers using a memory based learning algorithm that succeeds in reducing the error rate with 11.6% with respect to the best tagger involved.