合法化 pandas (Python) [英] Lemmatization Pandas (Python)
问题描述
我是Pandas的初学者，我试图弄清楚如何对数据框的单个列进行定标.以下面的示例为例(这是我想对词进行(非)常用词去除后的一些文本):
I am a beginner at Pandas and I am trying to figure out how to lemmatize a single column of my dataframe. Take the following example (this is some text after (un)common word removal which I'd like to lemmatize):
0个好的需求发生变化，自然酿造出纯天然啤酒...
0 good needs changes virgils natural micro brewe...
有1个新的喜欢的人给了惊喜，发现他们...
1 new favorite given delightful surprise find fl...
2个最喜欢的红酱享受强大的单宁ok拉...
2 red sauce favorite enjoy strong tannin ok pull...
3种品质出色的1800年代21世纪尝试饮料...
3 quality fantastic 1800s 21st century try drink...
4红第一次尝试恋爱100完美融合...
4 red first time trying love 100excellent blend ...
This is the code I use to do lemmatization (taken from here):
df['words'] = df['words'].apply(lambda x: "".join([Word(word).lemmatize() for word in x]))
df['words'].head()
但是运行此代码后，输出不会更改:
But once this code is run the output doesn't change:
0好的需求变化virgil天然微酿造啤酒...
0 good need change virgil natural micro brewed r...
有1个新的喜欢的人给了惊喜，发现他们...
1 new favorite given delightful surprise find fl...
2个最喜欢的红酱享受强大的单宁ok拉...
2 red sauce favorite enjoy strong tannin ok pull...
3种品质奇妙的1800年代21世纪尝试喝酒...
3 quality fantastic 1800s 21st century try drink...
4红第一次尝试恋爱100完美融合...
4 red first time trying love 100excellent blend ...
任何帮助将不胜感激:)
Any help would be greatly appreciated :)
P.S:words是标记词的列表
P.S: words is a list of tokenized words
推荐答案
您可能不再需要解决方案，但是如果要在许多pos上进行词素化，则可以使用:
You probably don't need anymore solution, but if you want to lemmatize on many pos, you can use:
如果您想要更多，可以尝试以下代码:
If you want more, you can try the following code:
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import wordnet
lemmatizer = nltk.stem.WordNetLemmatizer()
wordnet_lemmatizer = WordNetLemmatizer()
stop = stopwords.words('english')
def nltk_tag_to_wordnet_tag(nltk_tag):
if nltk_tag.startswith('J'):
return wordnet.ADJ
elif nltk_tag.startswith('V'):
return wordnet.VERB
elif nltk_tag.startswith('N'):
return wordnet.NOUN
elif nltk_tag.startswith('R'):
return wordnet.ADV
else:
return None
def lemmatize_sentence(sentence):
#tokenize the sentence and find the POS tag for each token
nltk_tagged = nltk.pos_tag(nltk.word_tokenize(sentence))
#tuple of (token, wordnet_tag)
wordnet_tagged = map(lambda x: (x[0], nltk_tag_to_wordnet_tag(x[1])), nltk_tagged)
lemmatized_sentence = []
for word, tag in wordnet_tagged:
if tag is None:
#if there is no available tag, append the token as is
lemmatized_sentence.append(word)
else:
#else use the tag to lemmatize the token
lemmatized_sentence.append(lemmatizer.lemmatize(word, tag))
return " ".join(lemmatized_sentence)
# Lemmatizing
df['Lemmatize'] = df['word'].apply(lambda x: lemmatize_sentence(x))
print(df.head())
df结果:
word | Lemmatize
0 Best scores, good cats, it rocks | Best score , good cat , it rock
1 You received best scores | You receive best score
2 Good news | Good news
3 Bad news | Bad news
4 I am loving it | I be love it
5 it rocks a lot | it rock a lot
6 it is still good to do better | it be still good to do good
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