Geodesic
Learning Syntactic Tagging of Macedonian Language
Computer Science and Information Systems, Tome 15 (2018) no. 3.

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This paper presents the creation of machine learning based systems for Part-of-speech tagging of Macedonian language. Four well-known PoS tagger systems implemented for English and Slavic languages: TnT, cyclic dependency network, guided learning framework for bidirectional sequence classification, and dynamic features induction were trained. Orwell’s novel “1984” was manually tagged from the authors and it was used split into training and test set. After the training of the models, a comparison between the models was made. At the end, a POS tagger with an accuracy that reaches 97.5% was achieved, making it very appropriate for the future grammatical tagging of the National corpus of Macedonian language, which is currently in its initial stage. The Part-of-speech tagger that was create is published online and free to use.
Keywords: Part-of-speech tagging, TnT tagger, Cyclic dependency network, Guided learning for bidirectional sequence classification, Dynamic features induction 
@article{CSIS_2018_15_3_a18,
     author = {Martin Bonchanoski and Katerina Zdravkova},
     title = {Learning {Syntactic} {Tagging} of {Macedonian} {Language}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {15},
     number = {3},
     year = {2018},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2018_15_3_a18/}
}
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Martin Bonchanoski; Katerina Zdravkova. Learning Syntactic Tagging of Macedonian Language. Computer Science and Information Systems, Tome 15 (2018) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2018_15_3_a18/