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我正在尝试在 R 中创建一个带有字符级二元组的文档特征矩阵。我的代码的最后一行需要永远运行并且永远不会完成。其他线路最多花费不到一分钟。我不知道该怎么办。任何意见，将不胜感激。

代码：

library(quanteda)
#Tokenise corpus by characters
character_level_tokens = quanteda::tokens(corpus, 
                                what = "character",
                                remove_punct = T,
                                remove_symbols = T,
                                remove_numbers = T,
                                remove_url = T,
                                remove_separators = T, 
                                split_hyphens = T)

#Convert tokens to characters
character_level_tokens = as.character(character_level_tokens)

#Keep A-Z, a-z letters
character_level_tokens = gsub("[^A-Za-z]","",character_level_tokens)

#Extract character-level bigrams
final_data_char_bigram = char_ngrams(character_level_tokens, n = 2L, concatenator = "")

#Create document-feature matrix (DFM)
dfm.final_data_char_bigram = dfm(final_data_char_bigram)


length(final_data_char_bigram)
[1] 37115571

head(final_data_char_bigram)
[1] "lo" "ov" "ve" "el" "ly" "yt"
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1 回答 1

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我没有您的输入语料库或可重复的示例，但这里是如何获得您想要的结果。如果这在您更大的语料库上也不能正常工作，我会感到非常惊讶。第一种方法使用 quantead 中的选择和 ngram 构造，而第二种方法使用tokenizers包中的字符shingle tokenizer。

library("quanteda")
## Package version: 2.0.1

dfm.final_data_char_bigram <- data_corpus_inaugural %>%
  tokens(what = "character") %>%
  tokens_keep("[A-Za-z]", valuetype = "regex") %>%
  tokens_ngrams(n = 2, concatenator = "") %>%
  dfm()

dfm.final_data_char_bigram
## Document-feature matrix of: 58 documents, 545 features (26.4% sparse) and 4 docvars.
##                  features
## docs              fe el ll lo ow wc ci  it  ti iz
##   1789-Washington 20 31 34 12 15  3 29  85 118  5
##   1793-Washington  1  1  7  1  4  1  2   8  12  1
##   1797-Adams      24 52 44 25 24  3 23 160 214  7
##   1801-Jefferson  34 49 60 35 31  7 34  91 116  8
##   1805-Jefferson  26 57 64 27 37  8 34 130 163 11
##   1809-Madison    11 29 37 15 17  1 21  62  82  3
## [ reached max_ndoc ... 52 more documents, reached max_nfeat ... 535 more features ]


# another way

dfm.final_data_char_bigram2 <- data_corpus_inaugural %>%
  tokenizers::tokenize_character_shingles(n = 2) %>%
  as.tokens() %>%
  dfm()

dfm.final_data_char_bigram2
## Document-feature matrix of: 58 documents, 701 features (41.9% sparse).
##                  features
## docs              fe el ll lo ow wc ci  it  ti iz
##   1789-Washington 20 31 34 12 15  3 29  85 118  5
##   1793-Washington  1  1  7  1  4  1  2   8  12  1
##   1797-Adams      24 52 44 25 24  3 23 160 214  7
##   1801-Jefferson  34 49 60 35 31  7 34  91 116  8
##   1805-Jefferson  26 57 64 27 37  8 34 130 163 11
##   1809-Madison    11 29 37 15 17  1 21  62  82  3
## [ reached max_ndoc ... 52 more documents, reached max_nfeat ... 691 more features ]
于 2020-06-13T16:01:29.720 回答