nltk - wordnet lemmatization and pos tagging in python

Question

I wanted to use wordnet lemmatizer in python and I have learnt that the default pos tag is NOUN and that it does not output the correct lemma for a verb, unless the pos tag is explicitly specified as VERB.

My question is what is the best shot inorder to perform the above lemmatization accurately?

I did the pos tagging using nltk.pos_tag and I am lost in integrating the tree bank pos tags to wordnet compatible pos tags. Please help

from nltk.stem.wordnet import WordNetLemmatizer
lmtzr = WordNetLemmatizer()
tagged = nltk.pos_tag(tokens)

I get the output tags in NN,JJ,VB,RB. How do I change these to wordnet compatible tags?

Also do I have to train nltk.pos_tag() with a tagged corpus or can I use it directly on my data to evaluate?

Answer 1

First of all, you can use nltk.pos_tag() directly without training it. The function will load a pretrained tagger from a file. You can see the file name with nltk.tag._POS_TAGGER:

nltk.tag._POS_TAGGER
>>> 'taggers/maxent_treebank_pos_tagger/english.pickle' 

As it was trained with the Treebank corpus, it also uses the Treebank tag set.

The following function would map the treebank tags to WordNet part of speech names:

from nltk.corpus import wordnet

def get_wordnet_pos(treebank_tag):

    if treebank_tag.startswith('J'):
        return wordnet.ADJ
    elif treebank_tag.startswith('V'):
        return wordnet.VERB
    elif treebank_tag.startswith('N'):
        return wordnet.NOUN
    elif treebank_tag.startswith('R'):
        return wordnet.ADV
    else:
        return ''

You can then use the return value with the lemmatizer:

from nltk.stem.wordnet import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
lemmatizer.lemmatize('going', wordnet.VERB)
>>> 'go'

Check the return value before passing it to the Lemmatizer because an empty string would give a KeyError.