Word2vec-获取相似度 [英] Word2vec - get rank of similarity

问题描述

鉴于我有一个word2vec模型(由gensim设计)，我想获得单词之间的排名相似度.例如，假设我有"desk"一词，与"desk"最相似的词是:

Given I got a word2vec model (by gensim), I want to get the rank similarity between to words. For example, let's say I have the word "desk" and the most similar words to "desk" are:

1. 表0.64
2. 主席0.61
3. book 0.59
4. 铅笔0.52

我想创建一个函数，以便:

I want to create a function such that:

f(办公桌，书本)= 3由于书本是第三个与办公桌最相似的词.是否存在?最有效的方法是什么?

f(desk,book) = 3 Since book is the 3rd most similar word to desk. Does it exists? what is the most efficient way to do this?

推荐答案

您可以使用 rank(entity1，entity2)获取距离-与索引相同.

You can use the rank(entity1, entity2) to get the distance - same as the index.

model.wv.rank(sample_word, most_similar_word)

---

这里不需要下面给出的单独功能.出于参考目的保留它.

---

A separate function as given below won't be necessary here. Keeping it for information sake.

假设您在一个元组列表中包含单词列表及其向量，由 model.wv.most_similar(sample_word)返回，如图所示

Assuming you have the list of words and their vectors in a list of tuples, returned by model.wv.most_similar(sample_word) as shown

[('table', 0.64), ('chair', 0.61), ('book', 0.59), ('pencil', 0.52)]

下面的函数接受样本单词和最相似的单词作为params，并返回在输出中存在的索引或等级(例如[2])

The following function accepts the sample word and the most similar word as params, and returns the index or rank (eg. [2]) if it's present in the output

def rank_of_most_similar_word(sample_word, most_similar_word):
    l = model.wv.most_similar(sample_word)
    return [x+1 for x, y in enumerate(l) if y[0] == most_similar_word]

sample_word = 'desk'
most_similar_word = 'book'
rank_of_most_similar_word(sample_word, most_similar_word)

注意:按照注释中的建议，在使用 model.wv.most_like()时，使用 topn = x 可获得前x个最相似的单词.

Note: use topn=x to get the top x most similar words while using model.wv.most_similar(), as suggested in the comments.

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