如何从R中的ngram标记列表中有效地删除停用词

Question

这是一个更好的方法来做一些我已经无法做到的事情的吸引力:使用"停用词"过滤一系列n-gram标记,以便n-gram中任何停用词术语的出现触发删除.

我非常希望有一个解决方案适用于unigrams和n-gram,虽然可以有两个版本,一个带有"固定"标志,另一个带有"正则表达式"标志.我将这个问题的两个方面放在一起,因为有人可能有一个解决方案尝试一种解决固定和正则表达式停用词模式的不同方法.

格式:

• 标记是一个字符向量列表,可以是unigrams,也可以是由_(下划线)字符连接的n-gram .

• 停用词是一个字符向量.现在我满足于让它成为一个固定的字符串,但是能够使用正则表达式格式化的停用词实现它将是一个很好的奖励.

期望输出:与输入标记匹配但与任何组件标记匹配的字符列表被删除.(这意味着unigram匹配,或与n-gram包含的术语之一匹配.)

构建的示例,测试数据以及工作代码和基准:

tokens1 <- list(text1 = c("this", "is", "a", "test", "text", "with", "a", "few", "words"), 
                text2 = c("some", "more", "words", "in", "this", "test", "text"))
tokens2 <- list(text1 = c("this_is", "is_a", "a_test", "test_text", "text_with", "with_a", "a_few", "few_words"), 
                text2 = c("some_more", "more_words", "words_in", "in_this", "this_text", "text_text"))
tokens3 <- list(text1 = c("this_is_a", "is_a_test", "a_test_text", "test_text_with", "text_with_a", "with_a_few", "a_few_words"),
                text2 = c("some_more_words", "more_words_in", "words_in_this", "in_this_text", "this_text_text"))
stopwords <- c("is", "a", "in", "this")

# remove any single token that matches a stopword
removeTokensOP1 <- function(w, stopwords) {
    lapply(w, function(x) x[-which(x %in% stopwords)])
}

# remove any word pair where a single word contains a stopword
removeTokensOP2 <- function(w, stopwords) {
    matchPattern <- paste0("(^|_)", paste(stopwords, collapse = "(_|$)|(^|_)"), "(_|$)")
    lapply(w, function(x) x[-grep(matchPattern, x)])
}

removeTokensOP1(tokens1, stopwords)
## $text1
## [1] "test"  "text"  "with"  "few"   "words"
## 
## $text2
## [1] "some"  "more"  "words" "test"  "text" 

removeTokensOP2(tokens1, stopwords)
## $text1
## [1] "test"  "text"  "with"  "few"   "words"
## 
## $text2
## [1] "some"  "more"  "words" "test"  "text" 

removeTokensOP2(tokens2, stopwords)
## $text1
## [1] "test_text" "text_with" "few_words"
## 
## $text2
## [1] "some_more"  "more_words" "text_text" 

removeTokensOP2(tokens3, stopwords)
## $text1
## [1] "test_text_with"
## 
## $text2
## [1] "some_more_words"

# performance benchmarks for answers to build on
require(microbenchmark)
microbenchmark(OP1_1 = removeTokensOP1(tokens1, stopwords),
               OP2_1 = removeTokensOP2(tokens1, stopwords),
               OP2_2 = removeTokensOP2(tokens2, stopwords),
               OP2_3 = removeTokensOP2(tokens3, stopwords),
               unit = "relative")
## Unit: relative
## expr      min       lq     mean   median       uq      max neval
## OP1_1 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000   100
## OP2_1 5.119066 3.812845 3.438076 3.714492 3.547187 2.838351   100
## OP2_2 5.230429 3.903135 3.509935 3.790143 3.631305 2.510629   100
## OP2_3 5.204924 3.884746 3.578178 3.753979 3.553729 8.240244   100

Answer 1

这不是一个真正的答案 - 更多的评论来回答rawr关于经历所有停用词组合的评论.使用更长的stopwords列表,使用类似的东西%in%似乎没有遭受维度问题.

library(purrr)
removetokenstst <- function(tokens, stopwords) 
  map2(tokens, 
       lapply(tokens3, function(x) { 
         unlist(lapply(strsplit(x, "_"), function(y) { 
           any(y %in% stopwords) 
         })) 
       }), 
       ~ .x[!.y])

require(microbenchmark)
microbenchmark(OP1_1 = removeTokensOP1(tokens1, morestopwords),
           OP2_1 = removeTokensOP2(tokens1, morestopwords),
           OP2_2 = removeTokensOP2(tokens2, morestopwords),
           OP2_3 = removeTokensOP2(tokens3, morestopwords),
           Ak_3 = removetokenstst(tokens3, stopwords),
           Ak_3msw = removetokenstst(tokens3, morestopwords),
           unit = "relative")

Unit: relative
    expr       min        lq       mean    median        uq      max neval
   OP1_1   1.00000   1.00000   1.000000  1.000000  1.000000  1.00000   100
   OP2_1 278.48260 176.22273  96.462854 79.787932 76.904987 38.31767   100
   OP2_2 280.90242 181.22013  98.545148 81.407928 77.637006 64.94842   100
   OP2_3 279.43728 183.11366 114.879904 81.404236 82.614739 72.04741   100
    Ak_3  15.74301  14.83731   9.340444  7.902213  8.164234 11.27133   100
 Ak_3msw  18.57697  14.45574  12.936594  8.513725  8.997922 24.03969   100

停用词

morestopwords = c("a", "about", "above", "after", "again", "against", "all", 
"am", "an", "and", "any", "are", "arent", "as", "at", "be", "because", 
"been", "before", "being", "below", "between", "both", "but", 
"by", "cant", "cannot", "could", "couldnt", "did", "didnt", "do", 
"does", "doesnt", "doing", "dont", "down", "during", "each", 
"few", "for", "from", "further", "had", "hadnt", "has", "hasnt", 
"have", "havent", "having", "he", "hed", "hell", "hes", "her", 
"here", "heres", "hers", "herself", "him", "himself", "his", 
"how", "hows", "i", "id", "ill", "im", "ive", "if", "in", "into", 
"is", "isnt", "it", "its", "its", "itself", "lets", "me", "more", 
"most", "mustnt", "my", "myself", "no", "nor", "not", "of", "off", 
"on", "once", "only", "or", "other", "ought", "our", "ours", 
"ourselves", "out", "over", "own", "same", "shant", "she", "shed", 
"shell", "shes", "should", "shouldnt", "so", "some", "such", 
"than", "that", "thats", "the", "their", "theirs", "them", "themselves", 
"then", "there", "theres", "these", "they", "theyd", "theyll", 
"theyre", "theyve", "this", "those", "through", "to", "too", 
"under", "until", "up", "very", "was", "wasnt", "we", "wed", 
"well", "were", "weve", "were", "werent", "what", "whats", "when", 
"whens", "where", "wheres", "which", "while", "who", "whos", 
"whom", "why", "whys", "with", "wont", "would", "wouldnt", "you", 
"youd", "youll", "youre", "youve", "your", "yours", "yourself", 
"yourselves", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", 
"k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", 
"x", "y", "z")