Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Memory-based learning: using similarity for smoothing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Do not forget: full memory in memory-based learning of word pronunciation
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
Shallow Parsing Using Probabilistic Grammatical Inference
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Shallow parsing using specialized hmms
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
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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In Brill's (1994) groundbreaking work on parts-of-speech tagging, the starting point was to assign each word its most common tag. An extension to this first step is to utilize the lexical context (i.e., words and punctuation) surrounding the word. This approach could obviously be used for ordering tags into higher order units (referred to as chunks) using chunk labels.