We can apply an off-the-shelf tagger from NLTK or combine multiple taggers to customize the tagging process. It is easy to directly use the built-in tagging function, pos_tag, as in: pos_tag(input_tokens), for instance. But behind the scene, it is actually a prediction from a pre-built supervised learning model. The model is trained based on a large corpus composed of words that are correctly tagged.
Reusing an earlier example, we can perform PoS tagging as follows:
>>> import nltk>>> tokens = word_tokenize(sent)>>> print(nltk.pos_tag(tokens))[('I', 'PRP'), ('am', 'VBP'), ('reading', 'VBG'), ('a', 'DT'), ('book', 'NN'), ('.', '.'), ('It', 'PRP'), ('is', 'VBZ'), ('Python', 'NNP'), ('Machine', 'NNP'), ('Learning', 'NNP'), ('By', ...
