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 maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Non-dictionary-based Thai word segmentation using decision trees
HLT '01 Proceedings of the first international conference on Human language technology research
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Infoxtract: A customizable intermediate level information extraction engine
Natural Language Engineering
A hidden Markov model based named entity recognition system: Bengali and Hindi as case studies
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
An Information-Extraction System for Urdu---A Resource-Poor Language
ACM Transactions on Asian Language Information Processing (TALIP)
Rule-based named entity recognition in Urdu
NEWS '10 Proceedings of the 2010 Named Entities Workshop
A vector space model for subjectivity classification in Urdu aided by co-training
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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Part of Speech (POS) tagging and Named Entity (NE) tagging have become important components of effective text analysis. In this paper, we propose a bootstrapped model that involves four levels of text processing for Urdu. We show that increasing the training data for POS learning by applying bootstrapping techniques improves NE tagging results. Our model overcomes the limitation imposed by the availability of limited ground truth data required for training a learning model. Both our POS tagging and NE tagging models are based on the Conditional Random Field (CRF) learning approach. To further enhance the performance, grammar rules and lexicon lookups are applied on the final output to correct any spurious tag assignments. We also propose a model for word boundary segmentation where a bigram HMM model is trained for character transitions among all positions in each word. The generated words are further processed using a probabilistic language model. All models use a hybrid approach that combines statistical models with hand crafted grammar rules.