我正在尝试使用 NLTK 的 WordNetLemmatizer 对句子中的所有单词进行词形还原。我有很多句子，但我只是使用第一句话来确保我正确执行此操作。这是我所拥有的：

train_sentences[0]

"Explanation Why edits made username Hardcore Metallica Fan reverted? They vandalisms, closure GAs I voted New York Dolls FAC. And please remove template talk page since I'm retired now.89.205.38.27"

所以现在我尝试按如下方式对每个单词进行词形还原：

lemmatizer = WordNetLemmatizer()
new_sent = [lemmatizer.lemmatize(word) for word in train_sentences[0].split()]
print(new_sent)

我回来了：

['Explanation', 'Why', 'edits', 'made', 'username', 'Hardcore', 'Metallica', 'Fan', 'reverted?', 'They', 'vandalisms,', 'closure', 'GAs', 'I', 'voted', 'New', 'York', 'Dolls', 'FAC.', 'And', 'please', 'remove', 'template', 'talk', 'page', 'since', "I'm", 'retired', 'now.89.205.38.27']

有几个问题：

1）为什么“编辑”没有转化为“编辑”？诚然，如果我这样做lemmatizer.lemmatize("edits")我回来了edits但很惊讶。

2）为什么“破坏行为”没有转化为“破坏行为”？这非常令人惊讶，因为如果我这样做lemmatizer.lemmatize("vandalisms")，我回来了vandalism...

任何澄清/指导都会很棒！

TL;DR

首先标记句子，然后使用词性标记作为词形还原的附加参数输入。

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')

有关如何以及为何需要 POS 标签的详细演练，请参阅https://www.kaggle.com/alvations/basic-nlp-with-nltk

或者，您可以使用pywsdtokenizer + lemmatizer，NLTK 的包装器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']

（版主请注意：我不能将此问题标记为重复nltk：如何将周围的单词融入上下文中进行词形还原？因为答案在那里没有被接受，但它是重复的）。