词嵌入是一种语言建模技术，用于将词映射到实数向量。它代表向量空间中具有多个维度的单词或短语。可以使用各种方法（如神经网络，共现矩阵，概率模型等）来生成单词嵌入。
Word2Vec包含用于生成单词嵌入的模型。这些模型是浅的两层神经网络，具有一个输入层，一个隐藏层和一个输出层。
# importing all necessary modules
from nltk.tokenize import sent_tokenize, word_tokenize
import warnings
warnings.filterwarnings(action = 'ignore')
import gensim
from gensim.models import Word2Vec
# Reads ‘alice.txt’ file
sample = open("C:\\Users\\Vishesh\\Desktop\\alice.txt", "r")
s = sample.read()
# Replaces escape character with space
f = s.replace("\n", " ")
data = []
# iterate through each sentence in the file
for i in sent_tokenize(f):
temp = []
# tokenize the sentence into words
for j in word_tokenize(i):
temp.append(j.lower())
data.append(temp)
# Create CBOW model
model1 = gensim.models.Word2Vec(data, min_count = 1, size = 100, window = 5)
# Print results
print("Cosine similarity between 'alice' " + "and 'wonderland' - CBOW : ", model1.similarity('alice', 'wonderland'))
print("Cosine similarity between 'alice' " + "and 'machines' - CBOW : ", model1.similarity('alice', 'machines'))
# Create Skip Gram model
model2 = gensim.models.Word2Vec(data, min_count = 1, size = 100, window =5, sg = 1)
# Print results
print("Cosine similarity between 'alice' " + "and 'wonderland' - Skip Gram : ", model2.similarity('alice', 'wonderland'))
print("Cosine similarity between 'alice' " + "and 'machines' - Skip Gram : ", model2.similarity('alice', 'machines'))