使用 Python 进行词嵌入：docc

import reimport numpy as npfrom gensim.models import doc2vecfrom gensim.models.doc2vec import taggeddocumentfrom nltk.corpus import gutenbergfrom multiprocessing import poolfrom scipy import spatial

sentences = list(gutenberg.sents('shakespeare-hamlet.txt'))   # import the corpus and convert into a listprint('type of corpus: ', type(sentences))print('length of corpus: ', len(sentences))

print(sentences[0])    # title, author, and yearprint(sentences[1])print(sentences[10])

for i in range(len(sentences)):    sentences[i] = [word.lower() for word in sentences[i] if re.match('^[a-za-z]+', word)]  print(sentences[0])    # title, author, and yearprint(sentences[1])print(sentences[10])

for i in range(len(sentences)):    sentences[i] = taggeddocument(words = sentences[i], tags = ['sent{}'.format(i)])    # converting each sentence into a taggeddocumentsentences[0]

model = doc2vec(documents = sentences,dm = 1, size = 100, min_count = 1, iter = 10, workers = pool()._processes)model.init_sims(replace = true)

model.save('doc2vec_model')model = doc2vec.load('doc2vec_model')

model.most_similar('hamlet')

v1 = model['king']v2 = model['queen']# define a function that computes cosine similarity between two wordsdef cosine_similarity(v1, v2):    return 1 - spatial.distance.cosine(v1, v2)cosine_similarity(v1, v2)