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dc.date.accessioned2019-07-10T08:21:16Z
dc.date.available2019-07-10T08:21:16Z
dc.date.issued2014
dc.identifier.citationYumusak, Semih, Erdogan Dogdu, and Halife Kodaz. "Tagging accuracy analysis on part-of-speech taggers." Journal of Computer and Communications 2.04 (2014): 157.en_US
dc.identifier.issn2327-5227
dc.identifier.urihttps://hdl.handle.net/20.500.12498/1048
dc.description.abstractPart of Speech (POS) Tagging can be applied by several tools and several programming languages. This work focuses on the Natural Language Toolkit (NLTK) library in the Python environment and the gold standard corpora installable. The corpora and tagging methods are analyzed and compared by using the Python language. Different taggers are analyzed according to their tagging accuracies with data from three different corpora. In this study, we have analyzed Brown, Penn Treebank and NPS Chat corpuses. The taggers we have used for the analysis are; default tagger, regex tagger, n-gram taggers. We have applied all taggers to these three corpuses, resultantly we have shown that whereas Unigram tagger does the best tagging in all corpora, the combination of taggers does better if it is correctly ordered. Additionally, we have seen that NPS Chat Corpus gives different accuracy results than the other two corpuses.en_US
dc.language.isoenen_US
dc.titleTagging accuracy analysis on part-of-speech taggersen_US
dc.typeArticleen_US


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