nltk: How to lemmatize taking surrounding words into context?

Question

The following code prints out leaf:

from nltk.stem.wordnet import WordNetLemmatizer

lem = WordNetLemmatizer()
print(lem.lemmatize('leaves'))

This may or may not be accurate depending on the surrounding context, e.g. Mary leaves the room vs. Dew drops fall from the leaves. How can I tell NLTK to lemmatize words taking surrounding context into account?

Answer 1

TL;DR

First tag the sentence, then use the POS tag as the additional parameter input for the lemmatization.

from nltk import pos_tag
from nltk.stem import WordNetLemmatizer

wnl = WordNetLemmatizer()

def penn2morphy(penntag):
    """ Converts Penn Treebank tags to WordNet. """
    morphy_tag = {'NN':'n', 'JJ':'a',
                  'VB':'v', 'RB':'r'}
    try:
        return morphy_tag[penntag[:2]]
    except:
        return 'n' 

def lemmatize_sent(text): 
    # Text input is string, returns lowercased strings.
    return [wnl.lemmatize(word.lower(), pos=penn2morphy(tag)) 
            for word, tag in pos_tag(word_tokenize(text))]

lemmatize_sent('He is walking to school')

For a detailed walkthrough of how and why the POS tag is necessary see https://www.kaggle.com/alvations/basic-nlp-with-nltk

---

Alternatively, you can use pywsd tokenizer + lemmatizer, a wrapper of NLTK's WordNetLemmatizer:

Install:

pip install -U nltk
python -m nltk.downloader popular
pip install -U pywsd

Code:

>>> from pywsd.utils import lemmatize_sentence
Warming up PyWSD (takes ~10 secs)... took 9.307677984237671 secs.

>>> text = "Mary leaves the room"
>>> lemmatize_sentence(text)
['mary', 'leave', 'the', 'room']

>>> text = 'Dew drops fall from the leaves'
>>> lemmatize_sentence(text)
['dew', 'drop', 'fall', 'from', 'the', 'leaf']