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



1 Answers

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.

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).




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)





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