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我有一个包含文章标题和相关 url 链接的数据框。

我的问题是相应标题的行中不需要url链接，例如：

               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

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，我将只使用unique函数。

我开始使用包中的levenshteinSim函数RecordLinkage，它为每一行给出一个相似度得分，但显然由于行不匹配，所以到处的相似度得分都很低。

我也从包中听说过这个stringdistmatrix功能，stringdist但不知道如何在这里使用它。

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1 回答 1

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当然可以优化，但这可能会让你开始：

  1. 函数matcher()convert 比较两个字符串并产生一个分数
  2. 之后我们将尝试匹配标题matcher()并获得最高分
  3. 如果无法找到高于阈值的分数，则让NA

在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
>
于 2018-04-08T19:54:49.667 回答