如何使用列表特征Python训练分类器

Question

标签和实体有两个变量。实体变量存储word的列表，该列表中的每个元素也包含list。所以它是一个列表变量列表。这个列表实际上是一个双gram特性，所以我需要保留它。在

我试着用这两个变量训练分类器。到目前为止我的代码是：

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

entity = [[['Prabowo Subianto']], [['Muhtar Ependi']], [['Nina Zatulini']], [['Partai Gerindra']], [['Persiba']], [['Partai Kebangkitan Bangsa (PKB)'], ['Partai Kebangkitan'], ['Kebangkitan Bangsa'], ['Bangsa ('], ['( PKB'], ['PKB )']], [['Sman 3 Kabupaten Tangerang'], ['Sman 3'], ['3 Kabupaten'], ['Kabupaten Tangerang']], [['Bandara Changi Singapura'], ['Bandara Changi'], ['Changi Singapura']], [['Warung Kopi Kita'], ['Warung Kopi'], ['Kopi Kita']]]
label = ['PERSON', 'PERSON', 'PERSON', 'ORGANIZATION', 'ORGANIZATION', 'ORGANIZATION', 'LOCATION', 'LOCATION', 'LOCATION']

vectorizer = TfidfVectorizer(min_df=1)
train_vector_entity = vectorizer.fit_transform(entity)
train_vector_label = label

classifier = svm.SVC()
classifier_word = classifier.fit(train_vector_entity,train_vector_label)

错误结果：

^{pr2}$

训练分类器的最佳方法是什么？ 谢谢

Answer 1

只需更改此行：

train_vector_entity = vectorizer.fit_transform([i[0][0] for i in entity])