如何解决Gensim 从单词列表计算质心
如何从词嵌入中计算给定 5 个词的质心,然后从该质心中找到最相似的词。 (在gensim中)
解决方法
from gensim.test.utils import datapath
from gensim import utils
class MyCorpus:
"""An iterator that yields sentences (lists of str)."""
def __iter__(self):
corpus_path = datapath('lee_background.cor')
for line in open(corpus_path):
# assume there's one document per line,tokens separated by whitespace
yield utils.simple_preprocess(line)
import gensim.models
sentences = MyCorpus()
model = gensim.models.Word2Vec(sentences=sentences)
word_vectors = model.wv
import numpy as np
centroid = np.average([word_vectors[w] for w in ['king','man','walk','tennis','victorian']],axis=0)
word_vectors.similar_by_vector(centroid)
在这种情况下会给你
[('man',0.9996674060821533),('by',0.9995684623718262),('over',0.9995648264884949),('from',0.9995632171630859),('were',0.9995599389076233),('who',0.99954754114151),('today',0.9995439648628235),('which',0.999538004398346),('on',0.9995279312133789),('being',0.9995211958885193)]
版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 dio@foxmail.com 举报,一经查实,本站将立刻删除。