使用TSNE嵌入可视化的单词不清楚

Question

我已经从Word Embeddings by M. Baroni et al.下载了预先训练的单词嵌入模型我想要可视化句子中存在的单词嵌入。我有两句话：

sentence1 = "Four people died in an accident."

sentence2 = "4 men are dead from a collision"

我有从上面的链接加载embeddings文件的功能：

def load_data(FileName = './EN-wform.w.5.cbow.neg10.400.subsmpl.txt'):

    embeddings = {}
    file = open(FileName,'r')
    i = 0
    print "Loading word embeddings first time"
    for line in file:
        # print line

        tokens = line.split('\t')

        #since each line's last token content '\n'
        # we need to remove that
        tokens[-1] = tokens[-1].strip()

        #each line has 400 tokens
        for i in xrange(1, len(tokens)):
            tokens[i] = float(tokens[i])

        embeddings[tokens[0]] = tokens[1:-1]
    print "finished"
    return embeddings

e = load_data()

从这两个句子中，我计算单词的词条并忽略停用词和标点符号，所以现在我的句子变为：

sentence1 = ['Four', 'people', 'died', 'accident']
sentence2 = ['4', 'men', 'dead', 'collision']

现在，当我尝试使用TSNE（t分布式随机邻居嵌入）来嵌入嵌入时，我首先为每个句子存储标签和标记：

#for sentence store labels and embeddings in list
# tokens contains vector of 400 dimensions for each label
labels1 = []
tokens1 = []
for i in sentence1:
    if i in e:
        labels1.append(i)
        tokens1.append(e[i])
    else:
        print i

labels2 = []
tokens2 = []
for i in sentence2:
    if i in e:
        labels2.append(i)
        tokens2.append(e[i])
    else:
        print i

For TSNE

tsne_model = TSNE(perplexity=40, n_components=2, init='random', n_iter=2000, random_state=23)
# fit transform for tokens of both sentences
new_values = tsne_model.fit_transform(tokens1)
new_values1 = tsne_model.fit_transform(tokens2)

#Plot values
x = []
y = []
x1 = []
y1 = []

for value in new_values:
    x.append(value[0])
    y.append(value[1])

for value in new_values1:
    x1.append(value[0])
    y1.append(value[1])


plt.figure(figsize=(10, 10)) 

for i in range(len(x)):
    plt.scatter(x[i],y[i])
    plt.annotate(labels[i],
                 xy=(x[i], y[i]),
                 xytext=(5, 2),
                 textcoords='offset points',
                 ha='right',
                 va='bottom')

for i in range(len(x1)):
    plt.scatter(x1[i],y1[i])
    plt.annotate(labels[i],
                 xy=(x1[i], y1[i]),
                 xytext=(5, 2),
                 textcoords='offset points',
                 ha='right',
                 va='bottom')

plt.show()

我的问题是，为什么诸如“碰撞”和“意外”，“人”和“人”之类的同义词有不同的坐标？如果单词是相同/同义词，它们不应该更接近吗？

distance = euclidean_distances（tokens1）＃returns shape（8,8）

Answer 1

来自TSNE-documentation：

t-SNE具有非凸的成本函数，即，通过不同的初始化，我们可以得到不同的结果。

这意味着在执行单词嵌入的维数减少时，不能保证获得相同的坐标。

要解决此问题，请通过加入句子来执行一次fit_transform而不是两次：

sentence1 = ['Four', 'people', 'died', 'accident']
sentence2 = ['4', 'men', 'dead', 'collision']
sentences = list(set(sentence1)| set(sentence2))

编辑：您的代码中还有一个错误，您正在绘制错误列表中的标签。