一、文本清洗与预处理

在自然语言处理中，文本清洗和预处理是必不可少的一步。因为原始文本中包含了各种各样的噪声、特殊符号等，这些都会对后续的自然语言处理产生干扰和误差。以下是一些文本清洗和预处理的技巧：

1、去除非文本部分，例如HTML标签

import re

def remove_html_tags(text):
    """去除HTML标签"""
    clean = re.compile('<.*?>')
    return re.sub(clean, '', text)

text = '
This is a headline.
This is a paragraph.
'
print(remove_html_tags(text))

2、去除特殊字符，如标点符号、数字等

import string

def remove_punctuation(text):
    """去掉标点符号"""
    return text.translate(str.maketrans('', '', string.punctuation))

text = "Let's try to remove punctuation from this text!"
print(remove_punctuation(text))

3、单词分词

import nltk

text = "This is a sentence for word tokenization."
tokens = nltk.word_tokenize(text)
print(tokens)

二、文本特征提取

文本特征提取是自然语言处理中的一个重要概念。在建立自然语言处理模型时，我们需要将文本转换为一些有意义的特征表示。以下是一些文本特征提取的技巧：

1、词袋模型

from sklearn.feature_extraction.text import CountVectorizer

corpus = [
    'This is the first document.',
    'This is the second second document.',
    'And the third one.',
    'Is this the first document?',
]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.toarray())

2、TF-IDF模型

from sklearn.feature_extraction.text import TfidfVectorizer

corpus = [
    'This is the first document.',
    'This is the second second document.',
    'And the third one.',
    'Is this the first document?',
]

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.toarray())

三、文本分类

文本分类是自然语言处理中的一个重要应用。在进行文本分类时，我们需要建立一个分类器，将文本自动归类到预定义的类别中。以下是一些文本分类的技巧：

1、朴素贝叶斯分类器

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

corpus = [
    'This is the first document.',
    'This document is the second document.',
    'And this is the third one.',
    'Is this the first document?',
]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)

clf = MultinomialNB()
clf.fit(X, [1, 1, 2, 2])

test_text = "Is this the third document?"
test_vec = vectorizer.transform([test_text])
print(clf.predict(test_vec))

2、支持向量机分类器

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

corpus = [
    'This is the first document.',
    'This document is the second document.',
    'And this is the third one.',
    'Is this the first document?',
]

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)

clf = LinearSVC()
clf.fit(X, [1, 1, 2, 2])

test_text = "Is this the third document?"
test_vec = vectorizer.transform([test_text])
print(clf.predict(test_vec))