python - 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).

Answer 1

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").