python - 如何使用 word2vec 训练分类器？

Question

该代码用于生成word2vec，并用它来训练朴素贝叶斯分类器。 我能够生成 word2vec 并成功使用相似性函数。下一步我想使用 word2vec 来训练朴素贝叶斯分类器。目前，当我试图在测试和训练中分割数据时，代码出错。如何将 word2vec 模型转换为数组，以便它可以用作训练数据。

#导入库 将 numpy 导入为 np 将 matplotlib.pyplot 导入为 plt 将 Pandas 导入为 pd 导入gensim

# Importing the dataset
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)

# Cleaning the texts
import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
corpus = []
for i in range(0, 1000):
    review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
    review = review.lower()
    review = review.split()
    ps = PorterStemmer()
    review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
#    for word2vec we want an array of vectors

    corpus.append(review)

#print(corpus)
X = gensim.models.Word2Vec(corpus, min_count=1,size=1000)
#print (X.most_similar("love"))


#embedding_matrix = np.zeros(len(X.wv.vocab), dtype='float32')
#for i in range(len(X.wv.vocab)):
#    embedding_vector = X.wv[X.wv.index2word[i]]
#    if embedding_vector is not None:
#        embedding_matrix[i] = embedding_vector

# Creating the Bag of Words model
#from sklearn.feature_extraction.text import CountVectorizer
#cv = CountVectorizer(max_features = 1500)
#X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, 1].values

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)

# Fitting Naive Bayes to the Training set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)

# Predicting the Test set results
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

It gives an error on line -
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
TypeError: Expected sequence or array-like, got <class 'gensim.models.word2vec.Word2Vec'>

Answer 1

Word2Vec 仅提供词嵌入。如果你想通过嵌入来表征文档，你需要对每个文档中所有单词的嵌入执行平均/求和/最大操作，以获得可用于分类的 D 维向量。参见 here和 there有关这方面的更多信息。

否则，您可以使用 Doc2Vec 模型直接生成文档嵌入，为此 gensim 也提供了一个非常好的提供程序。