如何使用python执行预处理步骤，例如删除停用词，删除标点符号，使用python中的词干化和词形还原。
我在csv文件中有文本数据，如段落和句子。我想做文字清理。
请通过在pandas dataframe中加载csv来举例说明
答案 0 :(得分：4)
可以通过一些命令轻松完成。另请注意，spacy不支持词干。您可以将此参考此thread
import spacy
nlp = spacy.load('en')
# sample text
text = """Lorem Ipsum is simply dummy text of the printing and typesetting industry. \
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown \
printer took a galley of type and scrambled it to make a type specimen book. It has survived not \
only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. \
It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, \
and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.\
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration \
in some form, by injected humour, or randomised words which don't look even slightly believable. If you are \
going to use a passage of Lorem Ipsum, you need to be sure there isn't anything embarrassing hidden in the \
middle of text. All the Lorem Ipsum generators on the Internet tend to repeat predefined chunks as necessary, \
making this the first true generator on the Internet. It uses a dictionary of over 200 Latin words, combined \
with a handful of model sentence structures, to generate Lorem Ipsum which looks reasonable. The generated \
Lorem Ipsum is therefore always free from repetition, injected humour, or non-characteristic words etc."""
# convert the text to a spacy document
document = nlp(text) # all spacy documents are tokenized. You can access them using document[i]
document[0:10] # = Lorem Ipsum is simply dummy text of the printing and
#the good thing about spacy is a lot of things like lemmatization etc are done when you convert them to a spacy document `using nlp(text)`. You can access sentences using document.sents
list(document.sents)[0]
# lemmatized words can be accessed using document[i].lemma_ and you can check
# if a word is a stopword by checking the `.is_stop` attribute of the word.
# here I am extracting the lemmatized form of each word provided they are not a stop word
lemmas = [token.lemma_ for token in document if not token.is_stop]
答案 1 :(得分：4)
到目前为止，我遇到的最好的管道来自Maksym Balatsko的中型文章Text preprocessing steps and universal reusable pipeline。最好的部分是我们可以将其用作scikit-learn变压器管道的一部分，并支持多进程：
import numpy as np
import multiprocessing as mp
import string
import spacy
import en_core_web_sm
from nltk.tokenize import word_tokenize
from sklearn.base import TransformerMixin, BaseEstimator
from normalise import normalise
nlp = en_core_web_sm.load()
class TextPreprocessor(BaseEstimator, TransformerMixin):
def __init__(self,
variety="BrE",
user_abbrevs={},
n_jobs=1):
"""
Text preprocessing transformer includes steps:
1. Text normalization
2. Punctuation removal
3. Stop words removal
4. Lemmatization
variety - format of date (AmE - american type, BrE - british format)
user_abbrevs - dict of user abbreviations mappings (from normalise package)
n_jobs - parallel jobs to run
"""
self.variety = variety
self.user_abbrevs = user_abbrevs
self.n_jobs = n_jobs
def fit(self, X, y=None):
return self
def transform(self, X, *_):
X_copy = X.copy()
partitions = 1
cores = mp.cpu_count()
if self.n_jobs <= -1:
partitions = cores
elif self.n_jobs <= 0:
return X_copy.apply(self._preprocess_text)
else:
partitions = min(self.n_jobs, cores)
data_split = np.array_split(X_copy, partitions)
pool = mp.Pool(cores)
data = pd.concat(pool.map(self._preprocess_part, data_split))
pool.close()
pool.join()
return data
def _preprocess_part(self, part):
return part.apply(self._preprocess_text)
def _preprocess_text(self, text):
normalized_text = self._normalize(text)
doc = nlp(normalized_text)
removed_punct = self._remove_punct(doc)
removed_stop_words = self._remove_stop_words(removed_punct)
return self._lemmatize(removed_stop_words)
def _normalize(self, text):
# some issues in normalise package
try:
return ' '.join(normalise(text, variety=self.variety, user_abbrevs=self.user_abbrevs, verbose=False))
except:
return text
def _remove_punct(self, doc):
return [t for t in doc if t.text not in string.punctuation]
def _remove_stop_words(self, doc):
return [t for t in doc if not t.is_stop]
def _lemmatize(self, doc):
return ' '.join([t.lemma_ for t in doc])
您可以将其用作：
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegressionCV
# ... assuming data split X_train, X_test ...
clf = Pipeline(steps=[
('normalize': TextPreprocessor(n_jobs=-1),
('features', TfidfVectorizer(ngram_range=(1, 2), sublinear_tf=True)),
('classifier', LogisticRegressionCV(cv=5,solver='saga',scoring='accuracy', n_jobs=-1, verbose=1))
])
clf.fit(X_train, y_train)
clf.predict(X_test)
X_train是将通过TextPreprocessing传递的数据，然后我们提取要素，然后传递给分类器。
答案 2 :(得分：2)
请阅读他们的文档，这是一个例子：
答案 3 :(得分：2)
这可能有助于谁在寻找这个问题的答案。
import spacy #load spacy
nlp = spacy.load("en", disable=['parser', 'tagger', 'ner'])
stops = stopwords.words("english")
def normalize(comment, lowercase, remove_stopwords):
if lowercase:
comment = comment.lower()
comment = nlp(comment)
lemmatized = list()
for word in comment:
lemma = word.lemma_.strip()
if lemma:
if not remove_stopwords or (remove_stopwords and lemma not in stops):
lemmatized.append(lemma)
return " ".join(lemmatized)
Data['Text_After_Clean'] = Data['Text'].apply(normalize, lowercase=True, remove_stopwords=True)