R:使用 jarowinkler 进行字符串模糊匹配 [英] R: String Fuzzy Matching using jarowinkler

问题描述

我在 R 中有两个字符类型的向量.

I have two vector of type character in R.

我希望能够使用 jarowinkler 将参考列表与原始字符列表进行比较，并分配一个百分比相似度分数.因此，例如，如果我有 10 个参考项和 20 个原始数据项，我希望能够获得比较的最佳分数以及算法与之匹配的分数(因此 2 个向量为 10).如果我有大小为 8 和 10 个参考项的原始数据，我应该只得到 8 个项的 2 个向量结果，每个项的匹配和得分最好

I want to be able to compare the reference list to the raw character list using jarowinkler and assign a % similarity score. So for example if i have 10 reference items and twenty raw data items, i want to be able to get the best score for the comparison and what the algorithm matched it to (so 2 vectors of 10). If i have raw data of size 8 and 10 reference items, i should only end up with a 2 vector result of 8 items with the best match and score per item

item、匹配、ma​​tched_to冰，78，冰淇淋

下面是我的代码，没什么可看的.

Below is my code which isn't much to look at.

NumItems.Raw = length(words)
NumItems.Ref = length(Ref.Desc)

for (item in words) 
{
  for (refitem in Ref.Desc)
  {
    jarowinkler(refitem,item)

    # Find Best match Score
    # Find Best Item in reference table
    # Add both items to vectors
    # decrement NumItems.Raw
    # Loop
  }
} 

推荐答案

使用玩具示例:

library(RecordLinkage)
library(dplyr)

ref <- c('cat', 'dog', 'turtle', 'cow', 'horse', 'pig', 'sheep', 'koala','bear','fish')
words <- c('dog', 'kiwi', 'emu', 'pig', 'sheep', 'cow','cat','horse')

wordlist <- expand.grid(words = words, ref = ref, stringsAsFactors = FALSE)
wordlist %>% group_by(words) %>% mutate(match_score = jarowinkler(words, ref)) %>%
summarise(match = match_score[which.max(match_score)], matched_to = ref[which.max(match_score)])

给予

 words     match matched_to
1   cat 1.0000000        cat
2   cow 1.0000000        cow
3   dog 1.0000000        dog
4   emu 0.5277778       bear
5 horse 1.0000000      horse
6  kiwi 0.5350000      koala
7   pig 1.0000000        pig
8 sheep 1.0000000      sheep

作为对 OP 评论的回应，最后一个命令使用来自 dplyr 的管道方法，并按原始词对原始单词和引用的每个组合进行分组词，添加一列 match_score 与 jarowinkler 分数，并仅返回最高匹配分数的摘要(由 which.max(match_score) 索引)，以及由最大 match_score 索引的引用.

As a response to the OP's comment, the last command uses the pipeline approach from dplyr, and groups every combination of the raw words and references by the raw words, adds a column match_score with the jarowinkler score, and returns only a summary of the highest match score (indexed by which.max(match_score)), as well as the reference which also is indexed by the maximum match_score.

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