在 R 中输出包含一元和二元的文本

Question

我试图弄清楚如何在 R 中的文本中识别一元和二元，然后根据阈值将两者保留在最终输出中。我已经用 gensim 的 Phraser 模型在 Python 中完成了这项工作，但还没有弄清楚如何在 R 中完成。

例如：

strings <- data.frame(text = 'This is a great movie from yesterday', 'I went to the movies', 'Great movie time at the theater', 'I went to the theater yesterday')
#Pseudocode below
bigs <- tokenize_uni_bi(strings, n = 1:2, threshold = 2)
print(bigs)
[['this', 'great_movie', 'yesterday'], ['went', 'movies'], ['great_movie', 'theater'], ['went', 'theater', 'yesterday']]

谢谢！

Answer 1

您可以为此使用 quanteda 框架：

library(quanteda)
# tokenize, tolower, remove stopwords and create ngrams
my_toks <- tokens(strings$text) 
my_toks <- tokens_tolower(my_toks)
my_toks <- tokens_remove(my_toks, stopwords("english"))
bigs <- tokens_ngrams(my_toks, n = 1:2)

# turn into document feature matrix and filter on minimum frequency of 2 and more
my_dfm <- dfm(bigs)
dfm_trim(my_dfm, min_termfreq = 2)

Document-feature matrix of: 4 documents, 6 features (50.0% sparse).
       features
docs    great movie yesterday great_movie went theater
  text1     1     1         1           1    0       0
  text2     0     0         0           0    1       0
  text3     1     1         0           1    0       1
  text4     0     0         1           0    1       1

# use convert function to turn this into a data.frame

或者，您可以使用 tidytext 包、tm、标记器等。这完全取决于您期望的输出。

使用 tidytext / dplyr 的示例如下所示：

library(tidytext)
library(dplyr)
strings %>% 
  unnest_ngrams(bigs, text, n = 2, n_min = 1, ngram_delim = "_", stopwords = stopwords::stopwords()) %>% 
  count(bigs) %>% 
  filter(n >= 2)

         bigs n
1       great 2
2 great_movie 2
3       movie 2
4     theater 2
5        went 2
6   yesterday 2

quanteda 和 tidytext 都有很多可用的在线帮助。请参阅 cran 上的两个包的小插曲。