熊猫的Word2vec数据

Question

我正在尝试应用word2vec来检查数据集每一行两列的相似性。

例如：

Sent1                                     Sent2
It is a sunny day                         Today the weather is good. It is warm outside
What people think about democracy         In ancient times, Greeks were the first to propose democracy  
I have never played tennis                I do not know who Roger Feder is 

要应用word2vec，我考虑以下几点：

import numpy as np

words1 = sentence1.split(' ')
words2 = sentence2.split(' ')
#The meaning of the sentence can be interpreted as the average of its words
sentence1_meaning = word2vec(words1[0])
count = 1
for w in words1[1:]:

    sentence1_meaning = np.add(sentence1_meaning, word2vec(w))
    count += 1
sentence1_meaning /= count

sentence2_meaning = word2vec(words1[0])
count = 1

for w in words1[1:]:
    sentence1_meaning = np.add(sentence1_meaning, word2vec(w))
    count += 1
sentence1_meaning /= count

sentence2_meaning = word2vec(words2[0])
count = 1
sentence2_meaning = word2vec(words2[0])
count = 1
for w in words2[1:]:
    sentence2_meaning = np.add(sentence2_meaning, word2vec(w))
    count += 1
sentence2_meaning /= count

#Similarity is the cosine between the vectors
similarity = np.dot(sentence1_meaning, sentence2_meaning)/(np.linalg.norm(sentence1_meaning)*np.linalg.norm(sentence2_meaning))

然而，这应该适用于两句话，而不是熊猫的数据。

你能告诉我在熊猫数据中应用word2vec需要做些什么来检查sent1和sent2之间的相似性吗？我想要一个关于结果的新专栏。

Answer 1

我没有word2vec的训练和可用，所以我将展示如何用一个假的word2vec来做你想做的事情，用tfidf权重将单词转换成句子。

步骤1。准备数据

from sklearn.feature_extraction.text import TfidfVectorizer
df = pd.DataFrame({"sentences": ["this is a sentence", "this is another sentence"]})

tfidf = TfidfVectorizer()
tfidf_matrix = tfidf.fit_transform(df.sentences).todense()
vocab = tfidf.vocabulary_
vocab
{'this': 3, 'is': 1, 'sentence': 2, 'another': 0}

步骤2。有伪造的word2vec (和我们的词汇量一样大)

word2vec = np.random.randn(len(vocab),300)

步骤3.为句子计算包含word2vec的列：

sent2vec_matrix = np.dot(tfidf_matrix, word2vec) # word2vec here contains vectors in the same order as in vocab
df["sent2vec"] = sent2vec_matrix.tolist()
df

sentences   sent2vec
0   this is a sentence  [-2.098592110459085, 1.4292324332403232, -1.10...
1   this is another sentence    [-1.7879436822159966, 1.680865619703155, -2.00...

步骤4.计算相似矩阵

from sklearn.metrics.pairwise import cosine_similarity
similarity = cosine_similarity(df["sent2vec"].tolist())
similarity
array([[1.        , 0.76557098],
       [0.76557098, 1.        ]])

要使word2vec工作，您需要稍微调整步骤2，以便word2vec以相同的顺序(按值或字母顺序指定)包含vocab中的所有单词。

就你的情况而言，应该是：

sorted_vocab = sorted([word for word,key in vocab.items()])
sorted_word2vec = []
for word in sorted_vocab:
    sorted_word2vec.append(word2vec[word])