如何使用 Quanteda 和 kwic 进行模糊模式匹配？

我有医生写的文本，我希望能够突出显示上下文中的特定单词（我在文本中搜索的单词之前 5 个单词和之后 5 个单词）。假设我想搜索“自杀”这个词。然后我将使用 quanteda 包中的 kwic 函数：

kwic（数据集，模式=“自杀”，窗口= 5）

到目前为止，一切都很好，但是我想考虑到打字错误的可能性。在这种情况下，我想允许使用三个不同的字符，并且对这些字符在单词中的位置没有限制。

是否可以使用 Quanteda 的 kwic 函数来做到这一点？

Example:

dataset <- data.frame("patient" = 1:9, "text" = c("On his first appointment, the patient was suicidal when he showed up in my office", 
                                  "On his first appointment, the patient was suicidaa when he showed up in my office",
                                  "On his first appointment, the patient was suiciaaa when he showed up in my office",
                                  "On his first appointment, the patient was suicaaal when he showed up in my office",
                                  "On his first appointment, the patient was suiaaaal when he showed up in my office",
                                  "On his first appointment, the patient was saacidal when he showed up in my office",
                                  "On his first appointment, the patient was suaaadal when he showed up in my office",
                                  "On his first appointment, the patient was icidal when he showed up in my office",
                                  "On his first appointment, the patient was uicida when he showed up in my office"))

dataset$text <- as.character(dataset$text)
kwic(dataset$text, pattern = "suicidal", window = 5)

只会给我第一个拼写正确的句子。

很好的问题。我们没有近似匹配作为“值类型”，但这对于未来的开发来说是一个有趣的想法。同时，我建议使用以下命令生成固定模糊匹配的列表base::agrep()然后匹配这些。所以这看起来像：

library("quanteda")
## Package version: 1.5.2

dataset <- data.frame(
  "patient" = 1:9, "text" = c(
    "On his first appointment, the patient was suicidal when he showed up in my office",
    "On his first appointment, the patient was suicidaa when he showed up in my office",
    "On his first appointment, the patient was suiciaaa when he showed up in my office",
    "On his first appointment, the patient was suicaaal when he showed up in my office",
    "On his first appointment, the patient was suiaaaal when he showed up in my office",
    "On his first appointment, the patient was saacidal when he showed up in my office",
    "On his first appointment, the patient was suaaadal when he showed up in my office",
    "On his first appointment, the patient was icidal when he showed up in my office",
    "On his first appointment, the patient was uicida when he showed up in my office"
  ),
  stringsAsFactors = FALSE
)
corp <- corpus(dataset)

# get unique words
vocab <- tokens(corp, remove_numbers = TRUE, remove_punct = TRUE) %>%
  types()

The use agrep()生成最接近的模糊匹配 - 在这里我运行了几次，增加max.distance每次都会稍微偏离默认值 0.1。

# get closest matches to "suicidal"
near_matches <- agrep("suicidal", vocab,
  max.distance = 0.3,
  ignore.case = TRUE, fixed = TRUE, value = TRUE
)
near_matches
## [1] "suicidal" "suicidaa" "suiciaaa" "suicaaal" "suiaaaal" "saacidal" "suaaadal"
## [8] "icidal"   "uicida"

然后，使用它作为pattern论证kwic():

# use these for fuzzy matching
kwic(corp, near_matches, window = 3)
##                                                        
##  [text1, 9] the patient was | suicidal | when he showed
##  [text2, 9] the patient was | suicidaa | when he showed
##  [text3, 9] the patient was | suiciaaa | when he showed
##  [text4, 9] the patient was | suicaaal | when he showed
##  [text5, 9] the patient was | suiaaaal | when he showed
##  [text6, 9] the patient was | saacidal | when he showed
##  [text7, 9] the patient was | suaaadal | when he showed
##  [text8, 9] the patient was |  icidal  | when he showed
##  [text9, 9] the patient was |  uicida  | when he showed

基于类似的解决方案还有其他可能性，例如模糊连接 or 字符串分布包，但这是一个简单的解决方案base应该工作得很好的包。