Gensim 从单词列表计算质心

编程问答 2022-05-17

问题描述

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

您应该查看Word2Vec gensim tutorial

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)]
gensimnlpword2vec