自然语言处理中的文本分类

声明：代码的运行环境为Python3。Python3与Python2在一些细节上会有所不同，希望广大读者注意。本博客以代码为主，代码中会有详细的注释。相关文章将会发布在我的个人博客专栏《Python自然语言处理》，欢迎大家关注。

1、首先来看一个使用朴素贝叶斯分类器对性别进行分类鉴定的例子。

# 构造特征提取器
def gender_features(word):  # 提取出字符串的最后一个字母
    return {'last_letter': word[-1]}

names_set = ([(name, 'male') for name in names.words('male.txt')] +
             [(name, 'female') for name in names.words('female.txt')])  # 获取语料库中人名的名称及性别

print(names_set[:10])
random.shuffle(names_set)  # 随机打乱names_set中的名字
print(names_set[:10])

featuresets = [(gender_features(n), g) for (n, g) in names_set]  # 选取特征集合
train_set, test_set = featuresets[500:], featuresets[:500]  # 抽取训练数据和测试数据
classifier = nltk.NaiveBayesClassifier.train(train_set)  # 朴素贝叶斯分类器
# 测试
classifier.classify(gender_features('Neo'))
classifier.classify(gender_features('Trinity'))

查看测试结果：

'male'
'female'

2、计算分类的正确率以及最有效的特征

# 分类正确率判断
print(nltk.classify.accuracy(classifier, test_set))

0.768

# 最有效的特征
classifier.show_most_informative_features(5)  # 输出5个最有效的特征

Most Informative Features
             last_letter = 'a'            female : male   =     40.2 : 1.0
             last_letter = 'k'              male : female =     30.8 : 1.0
             last_letter = 'f'              male : female =     16.6 : 1.0
             last_letter = 'p'              male : female =     11.9 : 1.0
             last_letter = 'v'              male : female =      9.8 : 1.0

3、当数据量很大时可以用如下方法进行数据集的划分。

# 大型数据时的数据集划分
from nltk.classify import apply_features

train_set = apply_features(gender_features, names_set[500:])
test_set = apply_features(gender_features, names_set[:500])

4、贝叶斯公式及代码实现。

贝叶斯公式如下图所示：

自然语言处理中的文本分类

贝叶斯分类器为：

自然语言处理中的文本分类

用代码的形式实现贝叶斯分类器：

# 手动计算贝叶斯分类器
# 计算P(特征|类别)
def f_c(data, fea, cla):
    cfd = nltk.ConditionalFreqDist((classes, features) for (features, classes) in data)
    return cfd[cla].freq(fea)

# 计算P(特征)
def p_feature(data, fea):
    fd = nltk.FreqDist(fea for (fea, cla) in data)
    return fd.freq(fea)

# 计算P(类别)
def p_class(data, cla):
    fd = nltk.FreqDist(cla for (fea, cla) in data)
    return fd.freq(cla)

# 计算P(类别│特征)
def res(data, fea, cla):
    return f_c(data, fea, cla) * p_class(data, cla) / p_feature(data, fea)

测试贝叶斯公式：

# 构造输入数据集
data = ([(name[-1], 'male') for name in names.words('male.txt')] +
        [(name[-1], 'female') for name in names.words('female.txt')])
random.shuffle(data)
train, test = data[500:], data[:500]

# 计算Neo的为男性的概率
res(train, 'k', 'male')
res(train, 'a', 'female')

测试结果为：

0.955223880597015
0.9829612220916567

5、选择正确的特征。

（1）过度拟合

# 过度拟合
def gender_features2(name):
    features = {}
    features["firstletter"] = name[0].lower()
    features["lastletter"] = name[-1].lower()
    for letter in 'abcdefghijklmnopqrstuvwxyz':
        features["count(%s)" % letter] = name.lower().count(letter)
        features["has(%s)" % letter] = (letter in name.lower())
    return features

featuresets = [(gender_features2(n), g) for (n, g) in names_set]
train_set, test_set = featuresets[500:], featuresets[:500]
classifier = nltk.NaiveBayesClassifier.train(train_set)
print(nltk.classify.accuracy(classifier, test_set))

结果：

0.76

（2）划分数据集，重新测试

# 数据划分为训练集、开发测试集、测试集
train_names = names_set[1500:]
devtest_names = names_set[500:1500]
test_names = names_set[:500]

# 重新训练模型
train_set = [(gender_features(n), g) for (n, g) in train_names]
devtest_set = [(gender_features(n), g) for (n, g) in devtest_names]
test_set = [(gender_features(n), g) for (n, g) in test_names]
classifier = nltk.NaiveBayesClassifier.train(train_set)
print(nltk.classify.accuracy(classifier, devtest_set))

测试结果：

0.761

（3）将测试出错的元素打印出来

# 打印错误列表
errors = []
for (name, tag) in devtest_names:
    guess = classifier.classify(gender_features(name))
    if guess != tag:
        errors.append((tag, guess, name))

for (tag, guess, name) in sorted(errors):  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    print('correct=%-8s guess=%-8s name=%-30s' % (tag, guess, name))

结果：

correct=female   guess=male     name=Adelind                       
correct=female   guess=male     name=Aeriel                        
correct=female   guess=male     name=Aeriell                       
correct=female   guess=male     name=Ag                            
correct=female   guess=male     name=Aidan                         
correct=female   guess=male     name=Allsun                        
correct=female   guess=male     name=Anabel                        
correct=female   guess=male     name=Ardelis                       
correct=female   guess=male     name=Aryn                          
correct=female   guess=male     name=Betteann                      
correct=female   guess=male     name=Bill                          
correct=female   guess=male     name=Blondell

（4）重构特征，进行训练

# 重新构建特征
def gender_features(word):
    return {'suffix1': word[-1:],
            'suffix2': word[-2:]}


# 重新训练模型
train_set = [(gender_features(n), g) for (n, g) in train_names]
devtest_set = [(gender_features(n), g) for (n, g) in devtest_names]
classifier = nltk.NaiveBayesClassifier.train(train_set)
print(nltk.classify.accuracy(classifier, devtest_set))

测试结果：

0.791

6、文档分类

from nltk.corpus import movie_reviews

documents = [(list(movie_reviews.words(fileid)), category)
             for category in movie_reviews.categories()
             for fileid in movie_reviews.fileids(category)]
random.shuffle(documents)

# 文档分类特征提取器
all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())
word_features = all_words.most_common()[:2000]


def document_features(document):
    document_words = set(document)
    features = {}
    for (word, freq) in word_features:
        features['contains(%s)' % word] = (word in document_words)
    return features


print(document_features(movie_reviews.words('pos/cv957_8737.txt')))

# 构造分类器
featuresets = [(document_features(d), c) for (d, c) in documents]
train_set, test_set = featuresets[100:], featuresets[:100]
classifier = nltk.NaiveBayesClassifier.train(train_set)
print(nltk.classify.accuracy(classifier, test_set))
print(classifier.show_most_informative_features(5))

测试结果：

0.86

Most Informative Features
   contains(outstanding) = True              pos : neg    =     10.4 : 1.0
        contains(seagal) = True              neg : pos    =      8.7 : 1.0
         contains(mulan) = True              pos : neg    =      8.1 : 1.0
   contains(wonderfully) = True              pos : neg    =      6.3 : 1.0
         contains(damon) = True              pos : neg    =      5.7 : 1.0
None

7、词性标注

# 词性标注
from nltk.corpus import brown

suffix_fdist = nltk.FreqDist()
for word in brown.words():
    word = word.lower()
    suffix_fdist[word[-1:]] += 1
    suffix_fdist[word[-2:]] += 1
    suffix_fdist[word[-3:]] += 1
common_suffixes = suffix_fdist.most_common()[:100]
print(common_suffixes)


# 定义特征提取器
def pos_features(word):
    features = {}
    for (suffix, freq) in common_suffixes:
        features['endswith(%s)' % suffix] = word.lower().endswith(suffix)
    return features


# 训练分类器
tagged_words = brown.tagged_words(categories='news')
featuresets = [(pos_features(n), g) for (n, g) in tagged_words]
size = int(len(featuresets) * 0.1)
train_set, test_set = featuresets[:1000], featuresets[2000:3000]
classifier = nltk.DecisionTreeClassifier.train(train_set)
print(nltk.classify.accuracy(classifier, test_set))
print(classifier.classify(pos_features('cats')))

结果：

0.611
NNS

决策树输出：

# 决策树输出
print(classifier.pseudocode(depth=4))

输出结果：

if endswith(he) == False: 
  if endswith(s) == False: 
    if endswith(.) == False: 
      if endswith(of) == False: return 'CD'
      if endswith(of) == True: return 'IN'
    if endswith(.) == True: return '.'
  if endswith(s) == True: 
    if endswith(as) == False: 
      if endswith('s) == False: return 'NPS'
      if endswith('s) == True: return 'NN$'
    if endswith(as) == True: 
      if endswith(was) == False: return 'CS'
      if endswith(was) == True: return 'BEDZ'
if endswith(he) == True: 
  if endswith(the) == False: return 'PPS'
  if endswith(the) == True: return 'AT'

8、根据上下文构造特征提取器

# 根据上下文构造特征提取器
def pos_features(sentence, i):
    features = {"suffix(1)": sentence[i][-1:],
                "suffix(2)": sentence[i][-2:],
                "suffix(3)": sentence[i][-3:]}
    if i == 0:
        features["prev-word"] = "<START>"
    else:
        features["prev-word"] = sentence[i - 1]
    return features


pos_features(brown.sents()[0], 8)

tagged_sents = brown.tagged_sents(categories='news')
featuresets = []
for tagged_sent in tagged_sents:
    untagged_sent = nltk.tag.untag(tagged_sent)
    for i, (word, tag) in enumerate(tagged_sent):
        featuresets.append((pos_features(untagged_sent, i), tag))
size = int(len(featuresets) * 0.1)
train_set, test_set = featuresets[size:], featuresets[:size]
classifier = nltk.NaiveBayesClassifier.train(train_set)
nltk.classify.accuracy(classifier, test_set)

结果：

0.7891596220785678

9、序列分类

# 序列分类

# 定义特征提取器
def pos_features(sentence, i, history):
    features = {"suffix(1)": sentence[i][-1:],
                "suffix(2)": sentence[i][-2:],
                "suffix(3)": sentence[i][-3:]}
    if i == 0:
        features["prev-word"] = "<START>"
        features["prev-tag"] = "<START>"
    else:
        features["prev-word"] = sentence[i - 1]
        features["prev-tag"] = history[i - 1]
    return features


# 构建序列分类器
class ConsecutivePosTagger(nltk.TaggerI):
    def __init__(self, train_sents):
        train_set = []
        for tagged_sent in train_sents:
            untagged_sent = nltk.tag.untag(tagged_sent)
            history = []
            for i, (word, tag) in enumerate(tagged_sent):
                featureset = pos_features(untagged_sent, i, history)
                train_set.append((featureset, tag))
                history.append(tag)
        self.classifier = nltk.NaiveBayesClassifier.train(train_set)

    def tag(self, sentence):
        history = []
        for i, word in enumerate(sentence):
            featureset = pos_features(sentence, i, history)
            tag = self.classifier.classify(featureset)
            history.append(tag)
        return zip(sentence, history)


tagged_sents = brown.tagged_sents(categories='news')
size = int(len(tagged_sents) * 0.1)
train_sents, test_sents = tagged_sents[size:], tagged_sents[:size]
tagger = ConsecutivePosTagger(train_sents)
print(tagger.evaluate(test_sents))

结果：

0.7980528511821975

10、句子分割

# 句子分割

# 获取已分割的句子数据
sents = nltk.corpus.treebank_raw.sents()
tokens = []
boundaries = set()
offset = 0
for sent in nltk.corpus.treebank_raw.sents():
    tokens.extend(sent)
    offset += len(sent)
    boundaries.add(offset - 1)


# 定义特征提取器
def punct_features(tokens, i):
    return {'next-word-capitalized': tokens[i + 1][0].isupper(),
            'prevword': tokens[i - 1].lower(),
            'punct': tokens[i],
            'prev-word-is-one-char': len(tokens[i - 1]) == 1}


# 定义标注
featuresets = [(punct_features(tokens, i), (i in boundaries))
               for i in range(1, len(tokens) - 1)
               if tokens[i] in '.?!']

# 构建分类器
size = int(len(featuresets) * 0.1)
train_set, test_set = featuresets[size:], featuresets[:size]
classifier = nltk.NaiveBayesClassifier.train(train_set)
nltk.classify.accuracy(classifier, test_set)

结果：

0.936026936026936

基于分类的断句器：

# 基于分类的断句器
def segment_sentences(words):
    start = 0
    sents = []
    for i, word in words:
        if word in '.?!' and classifier.classify(words, i) == True:
            sents.append(words[start:i + 1])
            start = i + 1
    if start < len(words):
        sents.append(words[start:])

11、识别对话行为类型

# 识别对话行为类型
posts = nltk.corpus.nps_chat.xml_posts()[:10000]

# 定义特征提取器
def dialogue_act_features(post):
    features = {}
    for word in nltk.word_tokenize(post):
        features['contains(%s)' % word.lower()] = True
    return features


# 训练分类器
featuresets = [(dialogue_act_features(post.text), post.get('class'))
               for post in posts]
size = int(len(featuresets) * 0.1)
train_set, test_set = featuresets[size:], featuresets[:size]
classifier = nltk.NaiveBayesClassifier.train(train_set)
print(nltk.classify.accuracy(classifier, test_set))

结果：

0.668

12、评估

（1）准确度

####评估####
# 创建训练集与测试集
import random
from nltk.corpus import brown

tagged_sents = list(brown.tagged_sents(categories='news'))
random.shuffle(tagged_sents)
size = int(len(tagged_sents) * 0.1)
train_set, test_set = tagged_sents[size:], tagged_sents[:size]

# 使用同类型文件
file_ids = brown.fileids(categories='news')
size = int(len(file_ids) * 0.1)
train_set = brown.tagged_sents(file_ids[size:])
test_set = brown.tagged_sents(file_ids[:size])

# 使用不同类型文件
train_set = brown.tagged_sents(categories='news')
test_set = brown.tagged_sents(categories='fiction')

##准确度##
names_set = ([(name, 'male') for name in names.words('male.txt')] +
             [(name, 'female') for name in names.words('female.txt')])
random.shuffle(names_set)

featuresets = [(gender_features(n), g) for (n, g) in names_set]
train_set, test_set = featuresets[500:], featuresets[:500]
classifier = nltk.NaiveBayesClassifier.train(train_set)
print('Accuracy: %4.2f' % nltk.classify.accuracy(classifier, test_set))

结果：

Accuracy: 0.78

（2）精确度与召回率

##精准度与召回率##
from sklearn.metrics import classification_report

test_set_fea = [features for (features, gender) in test_set]
test_set_gen = [gender for (features, gender) in test_set]
pre = classifier.classify_many(test_set_fea)
print(classification_report(test_set_gen, pre))

结果：

precision    recall  f1-score   support
     female       0.83      0.82      0.83       316
       male       0.70      0.71      0.70       184
avg / total       0.78      0.78      0.78       500

（3）混淆矩阵

##混淆矩阵##
cm = nltk.ConfusionMatrix(test_set_gen, pre)
print(cm)

结果：

|   f     |
       |   e     |
       |   m   m |
       |   a   a |
       |   l   l |
       |   e   e |
-------+---------+
female |<260> 56 |
  male |  54<130>|
-------+---------+
(row = reference; col = test)

（4）决策树中的熵和信息增益

# 熵和信息增益
import math
def entropy(labels):
    freqdist = nltk.FreqDist(labels)
    probs = [freqdist.freq(l) for l in nltk.FreqDist(labels)]
    return -sum([p * math.log(p, 2) for p in probs])
print(entropy(['male', 'male', 'male', 'male']))

-0.0

print(entropy(['male', 'female', 'male', 'male']))

0.8112781244591328

print(entropy(['female', 'male', 'female', 'male']))

1.0

print(entropy(['female', 'female', 'male', 'female']))

0.8112781244591328

print(entropy(['female', 'female', 'female', 'female']))

-0.0

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