如何实现Python底层技术的自然语言处理

import reimport nltkfrom nltk.corpus import stopwordsfrom nltk.tokenize import word_tokenizedef preprocess_text(text):    # 去除标点符号    text = re.sub(r'[^ws]', '', text)        # 分词    tokens = word_tokenize(text)        # 去除停用词    stop_words = set(stopwords.words('english'))    tokens = [token for token in tokens if token.lower() not in stop_words]        # 返回处理后的文本    return tokens

import nltkfrom nltk.tokenize import word_tokenizefrom nltk.tag import pos_tagdef pos_tagging(text):    # 分词    tokens = word_tokenize(text)        # 词性标注    tagged_tokens = pos_tag(tokens)        # 返回标注结果    return tagged_tokens

import nltkfrom nltk.tokenize import word_tokenizefrom nltk.chunk import ne_chunkdef named_entity_recognition(text):    # 分词    tokens = word_tokenize(text)        # 命名实体识别    tagged_tokens = pos_tag(tokens)    named_entities = ne_chunk(tagged_tokens)        # 返回识别结果    return named_entities

import nltkfrom nltk.corpus import movie_reviewsfrom nltk.tokenize import word_tokenizefrom nltk.classify import NaiveBayesClassifierfrom nltk.classify.util import accuracydef text_classification(text):    # 分词    tokens = word_tokenize(text)        # 获取特征集    features = {word: True for word in tokens}        # 加载情感分析数据集    positive_reviews = [(movie_reviews.words(fileid), 'positive') for fileid in movie_reviews.fileids('pos')]    negative_reviews = [(movie_reviews.words(fileid), 'negative') for fileid in movie_reviews.fileids('neg')]    dataset = positive_reviews + negative_reviews        # 构建训练数据集和测试数据集    training_data = dataset[:800]    testing_data = dataset[800:]        # 训练模型    classifier = NaiveBayesClassifier.train(training_data)        # 测试模型准确率    accuracy_score = accuracy(classifier, testing_data)        # 分类结果    sentiment = classifier.classify(features)        # 返回分类结果    return sentiment, accuracy_score