保留 R 语料库中的 EXACT 单词

Question

来自发布于：Keep document ID with R corpus by @MrFlick

我正在尝试稍微修改一个很好的例子。

问题：如何修改content_transformer函数只保留exact的话？您可以在 inspect 输出中看到，wonderful 被视为 wonder，ratio 被视为 rational。我对gregexpr和regmatches的理解不是很深。

创建数据框：

dd <- data.frame(
  id = 10:13,
  text = c("No wonderful, then, that ever",
           "So that in many cases such a ",
           "But there were still other and",
           "Not even at the rationale")
  , stringsAsFactors = F
)

现在，为了从 data.frame 中读取特殊属性，我们将使用 readTabular 函数来制作我们自己的自定义 data.frame reader

library(tm)
myReader <- readTabular(mapping = list(content = "text", id = "id"))

指定要用于内容的列和 data.frame 中的 ID。现在我们用 DataframeSource 读取它，但使用我们的自定义 reader.

tm <- VCorpus(DataframeSource(dd), readerControl = list(reader = myReader))

现在如果我们只想保留一组特定的单词，我们可以创建自己的 content_transformer 函数。一种方法是

  keepOnlyWords <- content_transformer(function(x, words) {
        regmatches(x, 
            gregexpr(paste0("\b(",  paste(words, collapse = "|"), "\b)"), x)
        , invert = T) <- " "
        x
    })

这会将不在单词列表中的所有内容替换为 space。请注意，在此之后您可能想要运行 stripWhitespace。因此我们的转换看起来像

keep <- c("wonder", "then", "that", "the")

tm <- tm_map(tm, content_transformer(tolower))
tm <- tm_map(tm, keepOnlyWords, keep)
tm <- tm_map(tm, stripWhitespace)

检查 dtm 矩阵：

> inspect(dtm)
<<DocumentTermMatrix (documents: 4, terms: 4)>>
Non-/sparse entries: 7/9
Sparsity           : 56%
Maximal term length: 6
Weighting          : term frequency (tf)

    Terms
Docs ratio that the wonder
  10     0    1   1      1
  11     0    1   0      0
  12     0    0   1      0
  13     1    0   1      0

Answer 1

将语法切换到 tidytext，您当前的转换是

library(tidyverse)
library(tidytext)
library(stringr)

dd %>% unnest_tokens(word, text) %>% 
    mutate(word = str_replace_all(word, setNames(keep, paste0('.*', keep, '.*')))) %>% 
    inner_join(data_frame(word = keep))

##   id   word
## 1 10 wonder
## 2 10    the
## 3 10   that
## 4 11   that
## 5 12    the
## 6 12    the
## 7 13    the

保持精确匹配更容易，因为您可以使用连接（使用 ==）而不是正则表达式：

dd %>% unnest_tokens(word, text) %>% 
    inner_join(data_frame(word = keep))

##   id word
## 1 10 then
## 2 10 that
## 3 11 that
## 4 13  the

要将其带回文档术语矩阵，

library(tm)

dd %>% mutate(id = factor(id)) %>%    # to keep empty rows of DTM
    unnest_tokens(word, text) %>% 
    inner_join(data_frame(word = keep)) %>% 
    mutate(i = 1) %>% 
    cast_dtm(id, word, i) %>% 
    inspect()

## <<DocumentTermMatrix (documents: 4, terms: 3)>>
## Non-/sparse entries: 4/8
## Sparsity           : 67%
## Maximal term length: 4
## Weighting          : term frequency (tf)
## 
##     Terms
## Docs then that the
##   10    1    1   0
##   11    0    1   0
##   12    0    0   0
##   13    0    0   1

目前，您的函数正在将 words 与 之前或 之后的边界相匹配。要将其更改为 之前和 之后，请更改 collapse 参数以包含边界：

tm <- VCorpus(DataframeSource(dd), readerControl = list(reader = myReader))

keepOnlyWords<-content_transformer(function(x,words) {
        regmatches(x, 
            gregexpr(paste0("(\b",  paste(words, collapse = "\b|\b"), "\b)"), x)
        , invert = T) <- " "
        x
    })

tm <- tm_map(tm, content_transformer(tolower))
tm <- tm_map(tm, keepOnlyWords, keep)
tm <- tm_map(tm, stripWhitespace)

inspect(DocumentTermMatrix(tm))

## <<DocumentTermMatrix (documents: 4, terms: 3)>>
## Non-/sparse entries: 4/8
## Sparsity           : 67%
## Maximal term length: 4
## Weighting          : term frequency (tf)
## 
##     Terms
## Docs that the then
##   10    1   0    1
##   11    1   0    0
##   12    0   0    0
##   13    0   1    0

Answer 2

我用 tm 得到了与@alistaire 相同的结果，在 keepOnlyWords 内容转换器中修改了以下行，首先由@BEMR 定义：

gregexpr(paste0("\b(",  paste(words, collapse = "|"), ")\b"), x)

@BEMR 首先指定的 gregexpr 中有一个错位的“)”，即应该是“)\\b”而不是“\\b)”

我认为上面的gregexpr等同于@alistaire指定的：

gregexpr(paste0("(\b",  paste(words, collapse = "\b|\b"), "\b)"), x)

保留 R 语料库中的 EXACT 单词

Keep EXACT words from R corpus

regex

r

corpus

text-mining