wordnet lemmatization and pos tagging in python

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]

@Suzana_K was working. But I there are some case result in KeyError as @ Clock Slave mention.

Convert treebank tags to Wordnet tag

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 None # for easy if-statement 

Now, we only input pos into lemmatize function only if we have wordnet tag

from nltk.stem.wordnet import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
tagged = nltk.pos_tag(tokens)
for word, tag in tagged:
    wntag = get_wordnet_pos(tag)
    if wntag is None:# not supply tag in case of None
        lemma = lemmatizer.lemmatize(word) 
    else:
        lemma = lemmatizer.lemmatize(word, pos=wntag) 

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)

You can do this in one line:

wnpos = lambda e: ('a' if e[0].lower() == 'j' else e[0].lower()) if e[0].lower() in ['n', 'r', 'v'] else 'n'

Then use wnpos(nltk_pos) to get the POS to give to .lemmatize(). In your case, lmtzr.lemmatize(word=tagged[0][0], pos=wnpos(tagged[0][1])).

You can create a map using the python default dict and take advantage of the fact that for the lemmatizer the default tag is Noun.

from nltk.corpus import wordnet as wn
from nltk.stem.wordnet import WordNetLemmatizer
from nltk import word_tokenize, pos_tag
from collections import defaultdict

tag_map = defaultdict(lambda : wn.NOUN)
tag_map['J'] = wn.ADJ
tag_map['V'] = wn.VERB
tag_map['R'] = wn.ADV

text = "Another way of achieving this task"
tokens = word_tokenize(text)
lmtzr = WordNetLemmatizer()

for token, tag in pos_tag(tokens):
    lemma = lmtzr.lemmatize(token, tag_map[tag[0]])
    print(token, "=>", lemma)

After searching from internet, I’ve found this solution: from sentence to “bag of words” derived after splitting, pos_tagging, lemmatizing and cleaning (from punctuation and “stopping words”) operations.
Here’s my code:

from nltk.corpus import wordnet as wn
from nltk.wsd import lesk
from nltk.stem import WordNetLemmatizer 
from nltk.corpus import stopwords 
from nltk.tokenize import word_tokenize

punctuation = u",.?!()-_"'\nrt;:+*<>@#§^$%&|/"
stop_words_eng = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()
tag_dict = {"J": wn.ADJ,
            "N": wn.NOUN,
            "V": wn.VERB,
            "R": wn.ADV}

def extract_wnpostag_from_postag(tag):
    #take the first letter of the tag
    #the second parameter is an "optional" in case of missing key in the dictionary 
    return tag_dict.get(tag[0].upper(), None)

def lemmatize_tupla_word_postag(tupla):
    """
    giving a tupla of the form (wordString, posTagString) like ('guitar', 'NN'), return the lemmatized word
    """
    tag = extract_wnpostag_from_postag(tupla[1])    
    return lemmatizer.lemmatize(tupla[0], tag) if tag is not None else tupla[0]

def bag_of_words(sentence, stop_words=None):
    if stop_words is None:
        stop_words = stop_words_eng
    original_words = word_tokenize(sentence)
    tagged_words = nltk.pos_tag(original_words) #returns a list of tuples: (word, tagString) like ('And', 'CC')
    original_words = None
    lemmatized_words = [ lemmatize_tupla_word_postag(ow) for ow in tagged_words ]
    tagged_words = None
    cleaned_words = [ w for w in lemmatized_words if (w not in punctuation) and (w not in stop_words) ]
    lemmatized_words = None
    return cleaned_words

sentence = "Two electric guitar rocks players, and also a better bass player, are standing off to two sides reading corpora while walking"
print(sentence, "nn bag of words:n", bag_of_words(sentence) )

You can do as following:

import nltk
from nltk.corpus import wordnet

wordnet_map = {
    "N": wordnet.NOUN,
    "V": wordnet.VERB,
    "J": wordnet.ADJ,
    "R": wordnet.ADV
}


def pos_tag_wordnet(text):
    """
        Create pos_tag with wordnet format
    """
    pos_tagged_text = nltk.pos_tag(text)

    # map the pos tagging output with wordnet output
    pos_tagged_text = [
        (word, wordnet_map.get(pos_tag[0])) if pos_tag[0] in wordnet_map.keys()
        else (word, wordnet.NOUN)
        for (word, pos_tag) in pos_tagged_text
    ]

    return pos_tagged_text