Question

我有一个数据框，其中包含文章标题和相关的网址链接。

我的问题是在相应标题的行中没有必要使用url链接，例如：

ridge/lasso

我的猜测是我需要考虑如此模糊的匹配逻辑，但我不确定如何。对于重复项，我将使用title | urls Who will be the next president? | https://website/5-ways-to-make-a-cocktail.com 5 ways to make a cocktail | https://website/who-will-be-the-next-president.com 2 millions raised by this startup | https://website/how-did-you-find-your-house.com How did you find your house | https://website/2-millions-raised-by-this-startup.com How did you find your house | https://washingtonpost/article/latest-movies-in-theater.com Latest movies in Theater | www.newspaper/mynews/what-to-cook-in-summer.com What to cook in summer | https://website/2-millions-raised-by-this-startup.com函数。

我开始使用unique包中的levenshteinSim函数，该函数为每行提供相似度分数，但显然行不匹配时，相似性得分在各地都很低。

我也听说过RecordLinkage包中的stringdistmatrix函数，但不知道如何在此处使用它。

Answer 1

肯定可以优化，但这可能会让你开始：

函数matcher() convert比较两个字符串并产生分数
之后我们会尝试将标题与matcher()匹配并获得最高分
如果无法找到高于阈值的分数，请收取NA

R

matcher <- function(needle, haystack) {
  ### Analyzes the url part, converts them to lower case words
  ### and calculates a score to return

  # convert url
  y <- unlist(strsplit(haystack, '/'))
  y <- tolower(unlist(strsplit(y[length(y)], '[-.]')))

  # convert needle
  x <- needle

  # sum it up
  (z <- (sum(x %in% y) / length(x) + sum(y %in% x) / length(y)) / 2)
}

pairer <- function(titles, urls, threshold = 0.75) {
  ### Calculates scores for each title -> url combination
  result <- vector(length = length(titles))
  for (i in seq_along(titles)) {
    needle <- tolower(unlist(strsplit(titles[i], ' ')))
    scores <- unlist(lapply(urls, function(url) matcher(needle, url)))
    high_score <- max(scores)

    # above threshold ?
    result[i] <- ifelse(high_score >= threshold, 
                        urls[which(scores == high_score)], NA)
  }
  return(result)
}

df$guess <- pairer(df$title, df$urls)
df

这会产生

                              title                                                        urls                                                       guess
1   Who will be the next president?               https://website/5-ways-to-make-a-cocktail.com          https://website/who-will-be-the-next-president.com
2         5 ways to make a cocktail          https://website/who-will-be-the-next-president.com               https://website/5-ways-to-make-a-cocktail.com
3 2 millions raised by this startup             https://website/how-did-you-find-your-house.com       https://website/2-millions-raised-by-this-startup.com
4       How did you find your house       https://website/2-millions-raised-by-this-startup.com             https://website/how-did-you-find-your-house.com
5       How did you find your house https://washingtonpost/article/latest-movies-in-theater.com             https://website/how-did-you-find-your-house.com
6          Latest movies in Theater             www.newspaper/mynews/what-to-cook-in-summer.com https://washingtonpost/article/latest-movies-in-theater.com
7            What to cook in summer       https://website/2-millions-raised-by-this-startup.com             www.newspaper/mynews/what-to-cook-in-summer.com
>

数据帧中的字符串模糊匹配

1 个答案: