Error-driven pruning of Treebank grammars for base noun phrase identification
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Merging Example-Based and Statistical Machine Translation: An Experiment
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
Shallow parsing with pos taggers and linguistic features
The Journal of Machine Learning Research
Shallow parsing using noisy and non-stationary training material
The Journal of Machine Learning Research
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
Learning computational grammars
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Translation spotting for translation memories
HLT-NAACL-PARALLEL '03 Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond - Volume 3
Searching for Part of Speech Tags That Improve Parsing Models
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
POS tagging of dialectal Arabic: a minimally supervised approach
Semitic '05 Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Syntactic chunking across different corpora
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
| Hi-index | 0.00 |
Treating shallow parsing as part-of-speech tagging yields results comparable with other, more elaborate approaches. Using the CoNLL 2000 training and testing material, our best model had an accuracy of 94.88%, with an overall FB1 score of 91.94%. The individual FB1 scores for NPs were 92.19%, VPs 92.70% and PPs 96.69%.