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 ...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.展开更多
One of the important tasks in Natural language processing is the part of speech tagging. For the Arabic language we have a lot of works but their performances do not rise to the required level, due to the complexity o...One of the important tasks in Natural language processing is the part of speech tagging. For the Arabic language we have a lot of works but their performances do not rise to the required level, due to the complexity of the task and the Arabic language characteristics. In this work we study a combination between two different approaches for Arabic POSTaggers. The first one is a maximum entropy-based one, and the second is a statistical/rule-based one. Fur-thermore, we add a knowledge-based method to annotate Arabic particles. Our idea improves the accuracy rate. We passed from almost 85% to almost 90% using our combined method, which seem promoter.展开更多
文摘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.
文摘One of the important tasks in Natural language processing is the part of speech tagging. For the Arabic language we have a lot of works but their performances do not rise to the required level, due to the complexity of the task and the Arabic language characteristics. In this work we study a combination between two different approaches for Arabic POSTaggers. The first one is a maximum entropy-based one, and the second is a statistical/rule-based one. Fur-thermore, we add a knowledge-based method to annotate Arabic particles. Our idea improves the accuracy rate. We passed from almost 85% to almost 90% using our combined method, which seem promoter.