Part-of-speech tagging using a Variable Memory Markov model

Authors:
Hinrich Schütze;Yoram Singer
Affiliations:
Center for the Study of Language and Information Stanford, CA;Hebrew University, Jerusalem
Venue:
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Year:
1994

Citing 5
Cited 11

On the Computational Complexity of Approximating Distributions by Probabilistic Automata

Machine Learning - Computational learning theory
Learning probabilistic automata with variable memory length

COLT '94 Proceedings of the seventh annual conference on Computational learning theory
On the learnability of discrete distributions

STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Coping with ambiguity and unknown words through probabilistic models

Computational Linguistics - Special issue on using large corpora: II
Automatic grammar induction and parsing free text: a transformation-based approach

ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics

Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

Computational Linguistics
On learning bounded-width branching programs

COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Shallow Parsing Using Probabilistic Grammatical Inference

ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Part-of-Speech Tagging with Evolutionary Algorithms

CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Mistake-driven mixture of hierarchical tag context trees

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
Self-organizing Markov models and their application to part-of-speech tagging

ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Self-organizing η-gram model for automatic word spacing

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Identifying semitic roots: Machine learning with linguistic constraints

Computational Linguistics
On prediction using variable order Markov models

Journal of Artificial Intelligence Research
Studying the advantages of a messy evolutionary algorithm for natural language tagging

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Part-of-Speech tagging of portuguese based on variable length markov chains

PROPOR'06 Proceedings of the 7th international conference on Computational Processing of the Portuguese Language

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new approach to disambiguating syntactically ambiguous words in context, based on Variable Memory Markov (VMM) models. In contrast to fixed-length Markov models, which predict based on fixed-lenth histories, variable memory Markov models dynamically adapt their history length based on the training data, and hence may use fewer parameters. In a test of a VMM based tagger on the Brown corpus, 95.81% of tokens are correctly classified.