如何计算两个字符串向量之间的余弦相似度

Question

我有 2 个维度为 6 的向量，我想要一个介于 0 和 1 之间的数字。

a=c("HDa","2Pb","2","BxU","BuQ","Bve")

b=c("HCK","2Pb","2","09","F","G")

谁能解释一下我应该怎么做？

Answer 1

您首先需要一个包含可能术语的字典，然后将您的向量转换为二进制向量，其中相应术语的位置为 1，其他位置为 0。如果将新向量命名为 a2 和 b2，则可以使用 cor(a2, b2) 类似地计算余弦，但请注意余弦同样介于 -1 和 1 之间。您可以将其映射到 [0 ,1] 像这样：0.5*cor(a2, b2) + 0.5

Answer 2

使用 lsa 软件包和该软件包的手册

# create some files
library('lsa')
td = tempfile()
dir.create(td)
write( c("HDa","2Pb","2","BxU","BuQ","Bve"), file=paste(td, "D1", sep="/"))
write( c("HCK","2Pb","2","09","F","G"), file=paste(td, "D2", sep="/"))

# read files into a document-term matrix
myMatrix = textmatrix(td, minWordLength=1)

编辑：显示 mymatrix 对象如何

myMatrix
#myMatrix
#       docs
#  terms D1 D2
#    2    1  1
#    2pb  1  1
#    buq  1  0
#    bve  1  0
#    bxu  1  0
#    hda  1  0
#    09   0  1
#    f    0  1
#    g    0  1
#    hck  0  1

# Calculate cosine similarity
res <- lsa::cosine(myMatrix[,1], myMatrix[,2])
res
#0.3333

Answer 3

CSString_vector <- c("Hi Hello","Hello");
corp <- tm::VCorpus(VectorSource(CSString_vector));
controlForMatrix <- list(removePunctuation = TRUE,wordLengths = c(1, Inf), weighting = weightTf)
dtm <- DocumentTermMatrix(corp,control = controlForMatrix);
matrix_of_vector = as.matrix(dtm);
res <- lsa::cosine(matrix_of_vector[1,], matrix_of_vector[2,]);

对于更大的数据集可能是更好的一个。

Answer 4

高级嵌入形式可能会帮助您获得更好的输出。请检查以下代码。它是一个通用句子编码模型，使用基于转换器的架构生成句子嵌入。

from absl import logging
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns

module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
print ("module %s loaded" % module_url)
def embed(input):
  return model([input])

paragraph = [
    "Universal Sentence Encoder embeddings also support short paragraphs. ",
    "Universal Sentence Encoder support paragraphs"]
messages = [paragraph]

print(np.inner( embed(paragraph[0]) , embed(paragraph[1])))

如何计算两个字符串向量之间的余弦相似度

How can I calculate Cosine similarity between two strings vectors

r

machine-learning

cosine-similarity