wordnet lemmatization and pos tagging in python

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pos tagging example

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.

Lemmatization Approaches with Examples in Python – Machine , nltk.pos_tag() returns a tuple with the POS tag. The key here is to map NLTK's POS tags to the format wordnet lemmatizer would accept. nltk - wordnet lemmatization and pos tagging in python - Stack Overflow 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

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]

Using NLTK for lemmatizing sentences - Gaurav Gupta, We would first find out the POS tag for each token using NLTK, use that to find the corresponding tag in WordNet and then use the lemmatizer to lemmatize the  3. Wordnet Lemmatizer with appropriate POS tag. It may not be possible manually provide the corrent POS tag for every word for large texts. So, instead, we will find out the correct POS tag for each word, map it to the right input character that the WordnetLemmatizer accepts and pass it as the second argument to lemmatize().

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)

Stemming, Lemmatisation and POS-tagging with Python and NLTK , Stemming, Lemmatisation and POS-tagging with Python and NLTK from nltk.​stem import PorterStemmer, WordNetLemmatizer ://stackoverflow.com/​questions/15586721/wordnet-lemmatization-and-pos-tagging-in-python. Questions: 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.

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)

Lemmatize whole sentences with Python and nltk's , from nltk.stem import WordNetLemmatizer There are some POS tags that correspond to words where the lemmatized form does not differ  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.

@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) 

nltk.stem.WordNetLemmatizer Python Example, Assuming these are compiled prior :param lemmatizer: an instance of an nltk WordNetLemmatizer() defaultPos = nltk.pos_tag(tokenlist) # get the POS tags  What is Lemmatization? Lemmatization is the algorithmic process of finding the lemma of a word depending on their meaning. Lemmatization usually refers to the morphological analysis of words, which aims to remove inflectional endings. It helps in returning the base or dictionary form of a word, which is known as the lemma.

Stemming and Lemmatization with Python NLTK, Real Time example showing use of Wordnet Lemmatization and POS Tagging in Python from nltk.corpus import wordnet as wn from  Stemming, lemmatisation and POS-tagging are important pre-processing steps in many text analytics applications. You can get up and running very quickly and include these capabilities in your Python applications by using the off-the-shelf solutions in offered by NLTK. Click to email this to a friend (Opens in new window)

Python, Below is the implementation of lemmatization words using NLTK: Python | PoS Tagging and Lemmatization using spaCy · Python | Gender Identification by  There is no option that you can pass to NLTK's POS-tagging and lemmatizing functions that will make them process other languages. One solution would be to get a training corpus for each language and to train your own POS-taggers with NLTK, then figure out a lemmatizing solution, maybe dictonary-based, for each language.

Example of stemming, lemmatisation and POS-tagging in NLTK , from nltk import pos_tag. from nltk.tokenize import word_tokenize. from nltk.stem import PorterStemmer, WordNetLemmatizer. stemmer = PorterStemmer(). Natural Language Tool Kit (NLTK) is a Python library to make programs that work with natural language. It provides a user-friendly interface to datasets that are over 50 corpora and lexical resources such as WordNet Word repository. The library can perform different operations such as tokenizing, stemming, classification, parsing, tagging, and

Comments
  • remember also satellite adjectives =) ADJ_SAT = 's' wordnet.princeton.edu/wordnet/man/wngloss.7WN.html
  • the pos tag for 'it' in the "I'm loving it." string is 'PRP'. The function returns an empty string which the lemmatizer doesn't accept and throws a KeyError. What can be done in that case?
  • Does anyone know how efficient this is when processing entire documents?
  • @ClockSlave: Don't put empty strings into the lemmatizer.
  • @alvas Which treebank tags should be mapped to the ADJ_SAT WordNet tag?
  • Or more generally: from nltk.corpus import wordnet; print wordnet._FILEMAP;
  • Why is ADJ_SAT not represented in the POST_LIST? What are examples for ADJ_SAT adjectives?
  • ADJ_SAT falls under Adjective cluster. You can read more about how adjective clusters are arranged here: wordnet.princeton.edu/documentation/wngloss7wn
  • to make this answer self-contained, remember import wn: from nltk.corpus import wordnet as wn
  • @pragMATHiC, included it. Thanks.