围绕定义的术语提取 n 个单词（多大小写）

Question

我有一个文本字符串向量，例如：

Sentences <- c("I would have gotten the promotion, but TEST my attendance wasn’t good enough.Let me help you with your baggage.",
               "Everyone was busy, so I went to the movie alone. Two seats were vacant.",
               "TEST Rock music approaches at high velocity.",
               "I am happy to take your TEST donation; any amount will be greatly TEST appreciated.",
               "A purple pig and a green donkey TEST flew a TEST kite in the middle of the night and ended up sunburnt.",
               "Rock music approaches at high velocity TEST.")

我要提取n（例如：三个）words（一个词的特点是字符前后有空格(s)) AROUND（即前后）特定术语（例如，'TEST'）。重要提示：Several matches 应该 allowed（即，如果某个特定术语出现多次，预期的解决方案应包含这些情况）。

结果可能是这样的（格式可以改进）：

S1  <- c(before = "the promotion, but", after = "my attendance wasn’t")
S2  <- c(before = "",                   after = "")
S3  <- c(before = "",                   after = "Rock music approaches")
S4a <- c(before = "to take your",       after = "donation; any amount")
S4b <- c(before = "will be greatly",    after = "appreciated.")
S5a <- c(before = "a green donkey",     after = "flew a TEST")
S5b <- c(before = "TEST flew",          after = "kite in the")
S6  <- c(before = "at high velocit",    after = "")

我该怎么做？我已经想出了其他 psot，它们是 o or relate to .

Answer 1

quanteda 包对此有一个很好的功能：kwic()（上下文中的关键字）。

开箱即用，这对您的示例非常有效：

library("quanteda")
names(Sentences) <- paste0("S", seq_along(Sentences))
(kw <- kwic(Sentences, "TEST", window = 3))
# 
# [S1, 9]   promotion, but | TEST | my attendance wasn't 
# [S3, 1]                  | TEST | Rock music approaches
# [S4, 7]     to take your | TEST | donation; any        
# [S4, 15] will be greatly | TEST | appreciated.         
# [S5, 8]   a green donkey | TEST | flew a TEST          
# [S5, 11]     TEST flew a | TEST | kite in the          
# [S6, 7] at high velocity | TEST | .               

(kw2 <- as.data.frame(kw)[, c("docname", "pre", "post")])
#   docname              pre                  post
# 1      S1  promotion , but  my attendance wasn't
# 2      S3                  Rock music approaches
# 3      S4     to take your        donation ; any
# 4      S4  will be greatly         appreciated .
# 5      S5   a green donkey           flew a TEST
# 6      S5      TEST flew a           kite in the
# 7      S6 at high velocity                     .

与您在问题中要求的单独对象相比，这可能是一种更好的格式。但是为了尽可能接近你的目标，你可以进一步变换如下。

# this picks up the empty matching sentence S2
(kw3 <- merge(kw2, 
              data.frame(docname = names(Sentences), stringsAsFactors = FALSE), 
              all.y = TRUE))
# replaces the NA with the empty string
kw4 <- as.data.frame(lapply(kw3, function(x) { x[is.na(x)] <- ""; x} ), 
                     stringsAsFactors = FALSE)
# renames pre/post to before/after
names(kw4)[2:3] <- c("before", "after")
# makes the docname unique
kw4$docname <- make.unique(kw4$docname)

kw4
#   docname           before                 after
# 1      S1  promotion , but  my attendance wasn't
# 2      S2                                       
# 3      S3                  Rock music approaches
# 4      S4     to take your        donation ; any
# 5    S4.1  will be greatly         appreciated .
# 6      S5   a green donkey           flew a TEST
# 7    S5.1      TEST flew a           kite in the
# 8      S6 at high velocity                     .

围绕定义的术语提取 n 个单词（多大小写）

Extract n Words Around Defined Term (Multicase)

text

r

text-mining

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