（三）word2vec词向量原理与实践
word2vec原理：
词向量提取工具，主要有两种模型。分别是CBOW和Skip-Gram。前者通过上下文预测中心词，后者通过中心词预测上下文。
代码：
texts = [['human', 'interface', 'computer'],
['survey', 'user', 'computer', 'system', 'response', 'time'],
['eps', 'user', 'interface', 'system'],
['system', 'human', 'system', 'eps'],
['user', 'response', 'time'],
['trees'],
['graph', 'trees'],
['graph', 'minors', 'trees'],
['graph', 'minors', 'survey']]
from gensim import corpora
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
print corpus[0] # [(0, 1), (1, 1), (2, 1)]
参考文献：
https://www.bilibili.com/video/av41393758/?p=2
https://github.com/Heitao5200/DGB/blob/master/feature/feature_code/train_word2vec.py