AMAZIGH PART-OF-SPEECH TAGGING USING MARKOV MODELS AND DECISION TREESijcsit
The main goal of this work is the implementation of a new tool for the Amazigh part of speech tagging using Markov Models and decision trees. After studying different approaches and problems of part of speech tagging, we have implemented a
tagging system based on TreeTagger - a generic stochastic tagging tool, very popular for its efficiency. We have gathered a working corpus, large enough to ensure a general linguistic coverage. This corpus has been used to run the tokenization process, as well as to train TreeTagger. Then, we performed a
straightforward outputs’ evaluation on a small test corpus. Though restricted, this evaluation showed really encouraging results.
AMAZIGH PART-OF-SPEECH TAGGING USING MARKOV MODELS AND DECISION TREESijcsit
The main goal of this work is the implementation of a new tool for the Amazigh part of speech tagging using Markov Models and decision trees. After studying different approaches and problems of part of speech tagging, we have implemented a
tagging system based on TreeTagger - a generic stochastic tagging tool, very popular for its efficiency. We have gathered a working corpus, large enough to ensure a general linguistic coverage. This corpus has been used to run the tokenization process, as well as to train TreeTagger. Then, we performed a
straightforward outputs’ evaluation on a small test corpus. Though restricted, this evaluation showed really encouraging results.