语言模型的雏形 N-Gram 和简单文本表示 Bag-of-Words
语言表示模型简介
(1) Bag-of-Words (BoW)
是什么？
- *定义 ：将文本表示为词频向量，忽略词序和语法，仅记录每个词的出现次数。
**示例：- 句子1：I love cats and cats love me.
- 句子2：Dogs love me too.
- 词表："I", "love", "cats", "and", "me", "dogs", "too"`
- BoW向量：
句子1 ：1, 2, 2, 1, 1, 0, 0
句子2 ：0, 1, 0, 0, 1, 1, 1
为什么需要？
- 简单高效：适合早期文本分类（如垃圾邮件识别、情感分析）。
- 可解释性强：词频直接反映文本主题。
- 局限性 ：
- 忽略词序 "猫吃鱼" "鱼吃猫" 向量表示在词袋表示中相同
- 高维稀疏（词表大时向量维度爆炸）。
(2) N-Gram
是什么？
- 定义 ：将文本分割为连续的N个词（或字符）组成的片段，捕捉局部上下文。
示例 （N=2）：- 句子："I love cats"
- Bigrams（2-grams）："I love", "love cats"`
- Trigrams（3-grams）："I love cats"`
为什么需要？
- 捕捉局部词序：比BoW更细致，能表达短语（如）。
- 建模上下文：通过统计N-Gram概率预测下一个词（语言模型）。
- 局限性 ：
- 数据稀疏性（长N-Gram在训练集中可能未出现）。
- 无法建模远距离依赖（如段落级关系）。
2. 项目实战：BoW与N-Gram的文本分类
任务目标
用BoW和Bigram特征对电影评论进行情感分类（正/负面），并比较效果。
代码实现
环境准备
bash
pip install numpy scikit-learn nltk数据集
使用简单的自定义数据集（实际项目可用IMDB数据集）：
python
# 自定义数据：0为负面，1为正面
texts = [
"I hate this movie", # 0
"This film is terrible", # 0
"I love this wonderful film",# 1
"What a great movie", # 1
]
labels = [0, 0, 1, 1]步骤1：Bag-of-Words特征提取
python
from sklearn.feature_extraction.text import CountVectorizer
# 创建BoW向量器
bow_vectorizer = CountVectorizer()
bow_features = bow_vectorizer.fit_transform(texts)
print("BoW特征词表：", bow_vectorizer.get_feature_names_out())
print("BoW特征矩阵：\n", bow_features.toarray())输出：
python
BoW特征词表： ['film' 'great' 'hate' 'is' 'love' 'movie' 'terrible' 'this' 'what' 'wonderful']
BoW特征矩阵：
[[0 0 1 0 0 1 0 1 0 0]
[1 0 0 1 0 0 1 1 0 0]
[1 0 0 0 1 0 0 1 0 1]
[0 1 0 0 0 1 0 0 1 0]]步骤2：Bigram特征提取
python
from sklearn.feature_extraction.text import CountVectorizer
# 创建Bigram向量器（N=2）
bigram_vectorizer = CountVectorizer(ngram_range=(2, 2))
bigram_features = bigram_vectorizer.fit_transform(texts)
print("Bigram特征词表：", bigram_vectorizer.get_feature_names_out())
print("Bigram特征矩阵：\n", bigram_features.toarray())输出：
python
Bigram特征词表： ['film is' 'hate this' 'is terrible' 'love this' 'terrible this'
'this movie' 'this wonderful' 'what great' 'wonderful film']
Bigram特征矩阵：
[[0 1 0 0 0 1 0 0 0]
[1 0 1 0 0 0 0 0 0]
[0 0 0 1 0 0 1 0 1]
[0 0 0 0 0 0 0 1 0]]步骤3：训练分类模型
python
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
# 划分训练集和测试集（此处仅演示，数据量小直接训练）
X_train_bow, X_test_bow = bow_features, bow_features # 实际需划分
X_train_bigram, X_test_bigram = bigram_features, bigram_features
y_train, y_test = labels, labels
# 训练BoW模型
model_bow = MultinomialNB()
model_bow.fit(X_train_bow, y_train)
print("BoW模型准确率：", model_bow.score(X_test_bow, y_test))
# 训练Bigram模型
model_bigram = MultinomialNB()
model_bigram.fit(X_train_bigram, y_train)
print("Bigram模型准确率：", model_bigram.score(X_test_bigram, y_test))输出：
python
BoW模型准确率： 1.0
Bigram模型准确率： 1.0
python
# 自定义数据：0为负面，1为正面
texts = [
"I hate this movie", # 0
"This film is terrible", # 0
"I love this wonderful film",# 1
"What a great movie", # 1
"I dislike this film", # 0
"This movie is amazing", # 1
"I enjoy this film", # 1
"This film is awful", # 0
"I adore this movie", # 1
"This film is fantastic", # 1
"I loathe this movie", # 0
"This movie is boring", # 0
"I appreciate this film", # 1
"This film is dreadful", # 0
"I cherish this movie", # 1
"This film is mediocre", # 0
"I detest this movie", # 0
"This film is superb", # 1
"I value this film", # 1
"This movie is subpar", # 0
"I respect this film", # 1
"This film is excellent", # 1
"I abhor this movie", # 0
"This film is lackluster", # 0
"I admire this film", # 1
"This movie is unsatisfactory", # 0
"I relish this film", # 1
"This film is remarkable", # 1
"I scorn this movie", # 0
"This film is outstanding", # 1
"I disapprove of this film", # 0
"This movie is unremarkable", # 0
"I treasure this film", # 1
"This film is commendable", # 1
"I find this movie distasteful", # 0
"This film is praiseworthy", # 1
"I think this movie is substandard", # 0
"This film is noteworthy", # 1
"I consider this movie to be poor", # 0
"This film is exceptional", # 1
"I feel this movie is inadequate", # 0
"This film is extraordinary", # 1
"I regard this movie as unsatisfactory", # 0
"This film is phenomenal", # 1
"I perceive this movie as disappointing", # 0
"This film is stellar", # 1
"I think this movie is mediocre" # 0
]
labels = [0, 0, 1, 1, 0,
1, 1, 0, 1, 1,
0, 0, 1, 0, 1,
0, 0, 1,1, 0,
1, 1, 0, 0, 1,
0, 1, 1, 0, 1,
0, 0, 1, 1, 0,
1, 0, 1,0,1,0,1,0,1,0,1,0]
print("文本数据：", len(texts), "条")
print("label：", len(labels), "条")
# 导入所需库
from sklearn.feature_extraction.text import CountVectorizer
# 创建BoW向量器
bow_vectorizer = CountVectorizer()
bow_features = bow_vectorizer.fit_transform(texts)
print("BoW特征词表：", bow_vectorizer.get_feature_names_out())
print("BoW特征矩阵：\n", bow_features.toarray())
# 创建Bigram向量器（N=2）
bigram_vectorizer = CountVectorizer(ngram_range=(2, 2))
bigram_features = bigram_vectorizer.fit_transform(texts)
print("Bigram特征词表：", bigram_vectorizer.get_feature_names_out())
print("Bigram特征矩阵：\n", bigram_features.toarray())
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
# 划分训练集和测试集（此处仅演示，数据量小直接训练）
train_test_split = 0.8
train_len = int(len(texts) * train_test_split)
X_train_bow, X_test_bow = bow_features[:train_len], bow_features[train_len:] # 实际需划分
X_train_bigram, X_test_bigram = bigram_features[:train_len], bigram_features[train_len:]
y_train, y_test = labels[:train_len], labels[train_len:]
# 训练BoW模型
model_bow = MultinomialNB()
model_bow.fit(X_train_bow, y_train)
print("BoW模型准确率：", model_bow.score(X_test_bow, y_test))
# 训练Bigram模型
model_bigram = MultinomialNB()
model_bigram.fit(X_train_bigram, y_train)
print("Bigram模型准确率：", model_bigram.score(X_test_bigram, y_test))3. 项目扩展与思考
(1) 分析结果
- BoW ：通过单个词区分情感（如
<font style="color:rgba(0, 0, 0, 0.9);">"hate"</font>表示负面，<font style="color:rgba(0, 0, 0, 0.9);">"love"</font>表示正面）。 - Bigram ：捕捉短语（如
<font style="color:rgba(0, 0, 0, 0.9);">"terrible this"</font>可能加强负面判断）。
(2) 改进方向
- 尝试更大的N（如Trigrams），观察是否过拟合。
- 使用TF-IDF代替词频，降低常见词的权重。
- 在真实数据集（如IMDB） 上测试效果。
4. 关键总结
- BoW：简单高效，适合基线模型，但忽略上下文。
- N-Gram：捕捉局部词序，但需权衡N的大小和稀疏性问题。
- 现代应用：两者仍用于轻量级任务（如快速原型），但深度模型（如RNN、Transformer）在复杂任务中更优。