摘要
Part 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 com- pared by using the Python language. Different taggers are analyzed according to their tagging ac- curacies 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.
Part 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 com- pared by using the Python language. Different taggers are analyzed according to their tagging ac- curacies 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.
