NLTK WordNet Lemmatizer: Shouldn't it lemmatize all inflections of a word?

Question

I'm using the NLTK WordNet Lemmatizer for a Part-of-Speech tagging project by first modifying each word in the training corpus to its stem (in place modification), and then training only on the new corpus. However, I found that the lemmatizer is not functioning as I expected it to.

For example, the word loves is lemmatized to love which is correct, but the word loving remains loving even after lemmatization. Here loving is as in the sentence "I'm loving it".

Isn't love the stem of the inflected word loving? Similarly, many other 'ing' forms remain as they are after lemmatization. Is this the correct behavior?

What are some other lemmatizers that are accurate? (need not be in NLTK) Are there morphology analyzers or lemmatizers that also take into account a word's Part Of Speech tag, in deciding the word stem? For example, the word killing should have kill as the stem if killing is used as a verb, but it should have killing as the stem if it is used as a noun (as in the killing was done by xyz).

Fred Foo · Accepted Answer · 2014-08-27T19:34:03.597

77

The WordNet lemmatizer does take the POS tag into account, but it doesn't magically determine it:

>>> nltk.stem.WordNetLemmatizer().lemmatize('loving')
'loving'
>>> nltk.stem.WordNetLemmatizer().lemmatize('loving', 'v')
u'love'

Without a POS tag, it assumes everything you feed it is a noun. So here it thinks you're passing it the noun "loving" (as in "sweet loving").

edited Aug 27 '14 at 19:34

answered Aug 27 '14 at 19:20

Fred Foo

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Thanks for the answer! Can you also tell, what are all the tags it take? n-nouns,v=verbs ...? – Abhishek Bhatia Sep 06 '15 at 21:53
@AbhishekBhatia You can use `WordNetCorpusReader.ADJ/ADJ_SAT/ADV/NOUN/VERB` (which have the values "a", "s", "r", "n", "v" respectively). – Danylo Mysak Oct 28 '21 at 10:30

score 33 · Answer 2 · answered Aug 28 '14 at 08:30

The best way to troubleshoot this is to actually look in Wordnet. Take a look here: Loving in wordnet. As you can see, there is actually an adjective "loving" present in Wordnet. As a matter of fact, there is even the adverb "lovingly": lovingly in Wordnet. Because wordnet doesn't actually know what part of speech you actually want, it defaults to noun ('n' in Wordnet). If you are using Penn Treebank tag set, here's some handy function for transforming Penn to WN tags:

from nltk.corpus import wordnet as wn

def is_noun(tag):
    return tag in ['NN', 'NNS', 'NNP', 'NNPS']


def is_verb(tag):
    return tag in ['VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']


def is_adverb(tag):
    return tag in ['RB', 'RBR', 'RBS']


def is_adjective(tag):
    return tag in ['JJ', 'JJR', 'JJS']


def penn_to_wn(tag):
    if is_adjective(tag):
        return wn.ADJ
    elif is_noun(tag):
        return wn.NOUN
    elif is_adverb(tag):
        return wn.ADV
    elif is_verb(tag):
        return wn.VERB
    return None

Hope this helps.

wnpos = lambda e: ('a' if e[0].lower() == 'j' else e[0].lower()) if e[0].lower() in ['n', 'r', 'v'] else 'x' — Alok Nayak, May 16 '15 at 19:58
However, it should be `wnpos = lambda e: ('a' if e[0].lower() == 'j' else e[0].lower()) if e[0].lower() in ['n', 'r', 'v'] else 'n'` because the default for the function is NOUN, not 'x' or `None`. — wordsforthewise, Dec 10 '17 at 00:11

score 6 · Answer 3 · answered Jan 20 '18 at 10:31

it's clearer and more effective than enumeration：

from nltk.corpus import wordnet

def get_wordnet_pos(self, 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 ''

def penn_to_wn(tag):
    return get_wordnet_pos(tag)

Kiran Racherla · Answer 4 · 2019-08-28T09:56:06.837

As an extension to the accepted answer from @Fred Foo above;

from nltk import WordNetLemmatizer, pos_tag, word_tokenize

lem = WordNetLemmatizer()
word = input("Enter word:\t")

# Get the single character pos constant from pos_tag like this:
pos_label = (pos_tag(word_tokenize(word))[0][1][0]).lower()

# pos_refs = {'n': ['NN', 'NNS', 'NNP', 'NNPS'],
#            'v': ['VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ'],
#            'r': ['RB', 'RBR', 'RBS'],
#            'a': ['JJ', 'JJR', 'JJS']}

if pos_label == 'j': pos_label = 'a'    # 'j' <--> 'a' reassignment

if pos_label in ['r']:  # For adverbs it's a bit different
    print(wordnet.synset(word+'.r.1').lemmas()[0].pertainyms()[0].name())
elif pos_label in ['a', 's', 'v']: # For adjectives and verbs
    print(lem.lemmatize(word, pos=pos_label))
else:   # For nouns and everything else as it is the default kwarg
    print(lem.lemmatize(word))

NLTK WordNet Lemmatizer: Shouldn't it lemmatize all inflections of a word?

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