Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Handling sparse data by successive abstraction
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Prototype-driven learning for sequence models
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
An efficient multi-agent system combining POS-Taggers for arabic texts
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Developing a competitive HMM arabic POS tagger using small training corpora
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Improving arabic part-of-speech tagging through morphological analysis
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Toward enhanced Arabic speech recognition using part of speech tagging
International Journal of Speech Technology
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Part Of Speech (POS) tagging is the ability to computationally determine which POS of a word is activated by its use in a particular context. POS tagger is a useful preprocessing tool in many natural languages processing (NLP) applications such as information extraction and information retrieval. In this paper, we present the preliminary achievement of Bigram Hidden Markov Model (HMM) to tackle the POS tagging problem of Arabic language. In addition, we have used different smoothing algorithms with HMM model to overcome the data sparseness problem. The Viterbi algorithm is used to assign the most probable tag to each word in the text. Furthermore, several lexical models have been defined and implemented to handle unknown word POS guessing based on word substring i.e. prefix probability, suffix probability or the linear interpolation of both of them. The average overall accuracy for this tagger is 95.8.