R:考虑使用标点符号进行分词

时间:2017-09-21 05:05:31

标签: r tm text-segmentation

我使用NGramTokenizer()进行1~3克分割,但似乎没有考虑标点符号,并删除标点符号。

因此,细分词对我来说并不理想。

(结果如:氧化剂氨基酸,氧化剂氨基酸,颗粒氧化剂等)。

是否有任何分段方式可以保留标点符号(我认为我可以使用POS标记来过滤掉分段工作后包含标点符号的字符串。)

或者有其他方式可以考虑使用标点符号进行分词吗?它会更多  对我来说很完美。

text <-  "the slurry includes: attrition pellet, oxidant, amino acid and water."

corpus_text <- VCorpus(VectorSource(text))
content(corpus_text[[1]])

BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 1, max = 3))
dtm <-  DocumentTermMatrix(corpus_text, control = list(tokenize = BigramTokenizer))
mat <- as.matrix(dtm)
colnames(mat)

 [1] "acid"                      "acid and"                  "acid and water"           
 [4] "amino"                     "amino acid"                "amino acid and"           
 [7] "and"                       "and water"                 "attrition"                
[10] "attrition pellet"          "attrition pellet oxidant"  "includes"                 
[13] "includes attrition"        "includes attrition pellet" "oxidant"                  
[16] "oxidant amino"             "oxidant amino acid"        "pellet"                   
[19] "pellet oxidant"            "pellet oxidant amino"      "slurry"                   
[22] "slurry includes"           "slurry includes attrition" "the"                      
[25] "the slurry"                "the slurry includes"       "water"    

2 个答案:

答案 0 :(得分:2)

您可以使用tokenize包的quanteda功能,如下所示:

library(quanteda)
text <- "some text, with commas, and semicolons; and even fullstop. to be toekinzed"
tokens(text, what = "word", remove_punct = FALSE, ngrams = 1:3)

输出:

tokens from 1 document.
text1 :
 [1] "some"              "text"              ","                 "with"             
 [5] "commas"            ","                 "and"               "semicolons"       
 [9] ";"                 "and"               "even"              "fullstop"         
[13] "."                 "to"                "be"                "toekinzed"        
[17] "some text"         "text ,"            ", with"            "with commas"      
[21] "commas ,"          ", and"             "and semicolons"    "semicolons ;"     
[25] "; and"             "and even"          "even fullstop"     "fullstop ."       
[29] ". to"              "to be"             "be toekinzed"      "some text ,"      
[33] "text , with"       ", with commas"     "with commas ,"     "commas , and"     
[37] ", and semicolons"  "and semicolons ;"  "semicolons ; and"  "; and even"       
[41] "and even fullstop" "even fullstop ."   "fullstop . to"     ". to be"          
[45] "to be tokeinzed"  

有关函数中每个参数的更多信息,请参阅documentation

<强>更新 有关文档术语频率,请查看Constructing a document-frequency matrix

作为示例,请尝试以下操作:

对于双字母(请注意,您不需要进行标记化):

dfm(text, remove_punct = FALSE, ngrams = 2, concatenator = " ")

答案 1 :(得分:1)

您可以在DTM之前通过tm_map传递语料库,例如

text <-  "the slurry includes: attrition pellet, oxidant, amino acid and water."

corpus_text <- VCorpus(VectorSource(text))
content(corpus_text[[1]])


clean_corpus <- function(corpus){
  corpus <- tm_map(corpus, removePunctuation) #other common punctuation
  corpus <- tm_map(corpus, stripWhitespace)
  corpus <- tm_map(corpus, removeWords, c(stopwords("en"), "and")) #ignoring "and"
  return(corpus)
}

corpus_text <- clean_corpus(corpus_text)
content(clean_corpus(corpus_text)[[1]])
#" slurry includes attrition pellet oxidant amino acid water"

BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 1, max = 3))
dtm <-  DocumentTermMatrix(corpus_text, control = list(tokenize = BigramTokenizer))
mat <- as.matrix(dtm)
colnames(mat)