A corpus-based approach to language learning
A corpus-based approach to language learning
Deterministic part-of-speech tagging with finite-state transducers
Computational Linguistics
Machine Learning
Lazy Transformation-Based Learning
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Part-of-Speech Tagging Using Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Improving data driven wordclass tagging by system combination
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Access to Multimedia Information through Multisource and Multilanguage Information Extraction
NLDB '02 Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers
Data & Knowledge Engineering - NLDB2002
Architectural elements of language engineering robustness
Natural Language Engineering
Transformation-based learning in the fast lane
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Using a text engineering framework to build an extendable and portable IE-based summarisation system
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
Adapting and extending lexical resources in a dialogue system
HLTKM '01 Proceedings of the workshop on Human Language Technology and Knowledge Management - Volume 2001
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Discovering Synonyms Based on Frequent Termsets
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Opinion analysis for business intelligence applications
OBI '08 Proceedings of the first international workshop on Ontology-supported business intelligence
Wordnet-based summarization of unstructured document
WSEAS Transactions on Computers
Predicting News Story Importance Using Language Features
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Learning domain ontologies for semantic Web service descriptions
Web Semantics: Science, Services and Agents on the World Wide Web
Discovering word meanings based on frequent termsets
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Using natural language processing for semantic indexing of scene-of-crime photographs
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Selecting keywords for content based recommendation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Automatic categorization and summarization of documentaries
Journal of Information Science
Semi-automatic financial events discovery based on lexico-semantic patterns
International Journal of Web Engineering and Technology
Ripple down rules for part-of-speech tagging
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Constrained atomic term: widening the reach of rule templates in transformation based learning
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Microblog-genre noise and impact on semantic annotation accuracy
Proceedings of the 24th ACM Conference on Hypertext and Social Media
NLP-based faceted search: Experience in the development of a science and technology search engine
Expert Systems with Applications: An International Journal
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This paper addresses the rule-based POS tagging method of Brill, and questions the importance of rule interactions to its performance. Adopting two assumptions that serve to exclude rule interactions during tagging and training, we arrive at some variants of Brill's approach that are instances of decision list models. These models allow for both rapid training on large data sets and rapid tagger execution, giving tagging accuracy that is comparable to, or better than the Brill method.