英文句子相似性判断

1.要求

　　本次项目提供一系列的英文句子对，每个句子对的两个句子，在语义上具有一定的相似性；每个句子对，获得一个在0-5之间的分值来衡量两个句子的语义相似性，打分越高说明两者的语义越相近。

如：

def stacking_result(w2v_x_train, LR_x_train,PCA_x_train, w2v_x_test, LR_x_test,PCA_x_test, label):
"""
stacking 方法对结果的集成
:param w2v_x_train:
:param LR_x_train:
:param w2v_x_test:
:param LR_x_test:
:param label:
:return:
"""
x_train = [[w2v_score, LR_score, PCA_score] for w2v_score, LR_score, PCA_score in zip(w2v_x_train, LR_x_train, PCA_x_train)]
x_test = [[w2v_score, LR_score, PCA_score] for w2v_score, LR_score, PCA_score in zip(w2v_x_test, LR_x_test, PCA_x_test)]
model = LinearRegression()
model.fit(x_train, label)
predicted_train = model.predict(x_train)
predicted_test = model.predict(x_test)

r, p = pearsonr(predicted_train, label)  # 直接结果输出
print('Result   stacking:raw', r)
return predicted_test

View Code
结果：

Result   w2v: 0.770842157582
Result   LR: 0.761403048811
Result   PCA : 0.728098131446
Result   stacking: 0.820756499196
end...