wordnet lemmatization and pos tagging in python

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?

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.

As in the source code of nltk.corpus.reader.wordnet (http://www.nltk.org/_modules/nltk/corpus/reader/wordnet.html)

#{ Part-of-speech constants
 ADJ, ADJ_SAT, ADV, NOUN, VERB = 'a', 's', 'r', 'n', 'v'
#}
POS_LIST = [NOUN, VERB, ADJ, ADV]

Steps to convert : Document->Sentences->Tokens->POS->Lemmas

import nltk
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet

#example text text = 'What can I say about this place. The staff of these restaurants is nice and the eggplant is not bad'

class Splitter(object):
    """
    split the document into sentences and tokenize each sentence
    """
    def __init__(self):
        self.splitter = nltk.data.load('tokenizers/punkt/english.pickle')
        self.tokenizer = nltk.tokenize.TreebankWordTokenizer()

    def split(self,text):
        """
        out : ['What', 'can', 'I', 'say', 'about', 'this', 'place', '.']
        """
        # split into single sentence
        sentences = self.splitter.tokenize(text)
        # tokenization in each sentences
        tokens = [self.tokenizer.tokenize(sent) for sent in sentences]
        return tokens


class LemmatizationWithPOSTagger(object):
    def __init__(self):
        pass
    def get_wordnet_pos(self,treebank_tag):
        """
        return WORDNET POS compliance to WORDENT lemmatization (a,n,r,v) 
        """
        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:
            # As default pos in lemmatization is Noun
            return wordnet.NOUN

    def pos_tag(self,tokens):
        # find the pos tagginf for each tokens [('What', 'WP'), ('can', 'MD'), ('I', 'PRP') ....
        pos_tokens = [nltk.pos_tag(token) for token in tokens]

        # lemmatization using pos tagg   
        # convert into feature set of [('What', 'What', ['WP']), ('can', 'can', ['MD']), ... ie [original WORD, Lemmatized word, POS tag]
        pos_tokens = [ [(word, lemmatizer.lemmatize(word,self.get_wordnet_pos(pos_tag)), [pos_tag]) for (word,pos_tag) in pos] for pos in pos_tokens]
        return pos_tokens

lemmatizer = WordNetLemmatizer()
splitter = Splitter()
lemmatization_using_pos_tagger = LemmatizationWithPOSTagger()

#step 1 split document into sentence followed by tokenization
tokens = splitter.split(text)

#step 2 lemmatization using pos tagger 
lemma_pos_token = lemmatization_using_pos_tagger.pos_tag(tokens)
print(lemma_pos_token)