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法律研究Python——使用Word2Vec进行词嵌入
词嵌入是一种语言模型技术，用于将单词映射到实数向量。它使用多个维度在向量空间中表示单词或短语。可以使用神经网络、共现矩阵、概率模型等各种方法生成词嵌入。
Word2Vec 由用于生成单词嵌入的模型组成。这些模型是浅层两层神经网络，具有一个输入层、一个隐藏层和一个输出层。
示例
# 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'))
更新于：08-8-2020
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