A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Connectionist Model for Part of Speech Tagging
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Chunking with maximum entropy models
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
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Conditional random fields (CRFs) is a framework for building probabilistic models to segment and label sequence data. CRFs offer several advantages over hidden Markov models (HMMs) and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. CRFs also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. In this paper we propose the Language Models developed for Part Of Speech (POS) tagging and chunking using CRFs for Tamil. The Language models are designed based on morphological information. The CRF based POS tagger has an accuracy of about 89.18%, for Tamil and the chunking process performs at an accuracy of 84.25% for the same language.