Как выполнить предварительную обработку текста с помощью spaCy?

Это может помочь:

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)

Лучший конвейер, с которым я когда-либо сталкивался, взят из статьи Максима Балацко на Medium Шаги предварительной обработки текста и универсальный многоразовый трубопровод. Самое приятное то, что мы можем использовать его как часть конвейера преобразователя scikit-learn и поддерживать многопроцессорность:

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])

Вы можете использовать его как:

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 - это данные, которые проходят через TextPreprocessing, затем мы извлекаем функции, а затем передаем их классификатору.

Это легко сделать с помощью нескольких команд. Также обратите внимание, что spacy не поддерживает стемминг. Вы можете сослаться на это в ветке

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]

Пожалуйста, прочтите их документы, вот один пример:

https://nicschrading.com/project/Intro-to-NLP-with-spaCy/