我正在尝试使用R的quanteda包创建3克。
我正在努力寻找一种方法来保留n-gram开头和结尾的句子标记，<s>和</s>，如下面的代码所示。
我认为使用与keptFeatures匹配的正则表达式的quanteda应该保留它们，但总是去除V形标记。
如何保持V形标记不被删除或用docfreq(mydfm)分隔句子开头和结尾的最佳方法是什么？
作为奖励问题，colSums(mydfm)优于Named num [1:n]的优势是什么，str（colSums（mydfm））和str（docfreq（mydfm））的结果几乎相同（{{1}前者，Named int [1:n]后者）？
library(quanteda)
text <- "<s>I'm a sentence and I'd better be formatted properly!</s><s>I'm a second sentence</s>"
qc <- corpus(text)
mydfm <- dfm(qc, ngram=3, removeNumbers = F, stem=T, keptFeatures="\\</?s\\>")
names(colSums(mydfm))
# Output:
# [1] "s_i'm_a" "i'm_a_sentenc" "a_sentenc_and" "sentenc_and_i'd"
# [2] "and_i'd_better" "i'd_better_be" "better_be_format"
# [3] "be_format_proper" "format_proper_s" "proper_s_s" "s_s_i'm"
# [4] "i'm_a_second" "a_second_sentenc" "second_sentenc_s"
修改
将keepFeatures更正为在代码段中保留功能。
答案 0 :(得分：2)
要返回简单的向量，只需取消列出tokenizedText" object returned from tokenize（）(which is a specially classed list, with additional attributes). Here I used the what =“fasterword”which splits on "\\s" -- it's a tiny bit smarter than what =“fasterword”which splits on“”`。< / p>
# how to not remove the <s>, and return a vector
unlist(toks <- tokenize(text, ngrams = 3, what = "fasterword"))
## [1] "<s>I'm_a_sentence" "a_sentence_and"
## [3] "sentence_and_I'd" "and_I'd_better"
## [5] "I'd_better_be" "better_be_formatted"
## [7] "be_formatted_properly!</s><s>I'm" "formatted_properly!</s><s>I'm_a"
## [9] "properly!</s><s>I'm_a_second" "a_second_sentence</s>"
要将其保留在句子中，请将对象标记两次，第一次按句子标记，第二次按fasterword标记。
# keep it within sentence
(sents <- unlist(tokenize(text, what = "sentence")))
## [1] "<s>I'm a sentence and I'd better be formatted properly!"
## [2] "</s><s>I'm a second sentence</s>"
tokenize(sents, ngrams = 3, what = "fasterword")
## tokenizedText object from 2 documents.
## Component 1 :
## [1] "<s>I'm_a_sentence" "a_sentence_and" "sentence_and_I'd" "and_I'd_better"
## [5] "I'd_better_be" "better_be_formatted" "be_formatted_properly!"
##
## Component 2 :
## [1] "</s><s>I'm_a_second" "a_second_sentence</s>"
要保留dfm中的V形标记，您可以通过tokenize()调用中上面使用的相同选项，因为dfm()调用tokenize()但具有不同的默认值 - 它使用了大多数用户可能想要的，而tokenize()更加保守。
# Bonus questions:
myDfm <- dfm(text, verbose = FALSE, what = "fasterword", removePunct = FALSE)
# "chevron" markers are not removed
features(myDfm)
## [1] "<s>i'm" "a" "sentence" "and" "i'd"
## [6] "better" "be" "formatted" "properly!</s><s>i'm" "second"
## [11] "sentence</s>"
红利问题的最后一部分是docfreq()和colSums()之间的区别。前者返回术语出现的文档计数，后者对列进行求和以获得跨文档的总术语频率。请参阅下文，"representatives"这两个词的不同之处。
# Difference between docfreq() and colSums():
myDfm2 <- dfm(inaugTexts[1:4], verbose = FALSE)
myDfm2[, "representatives"]
docfreq(myDfm2)["representatives"]
colSums(myDfm2)["representatives"]
## Document-feature matrix of: 4 documents, 1 feature.
## 4 x 1 sparse Matrix of class "dfmSparse"
## features
## docs representatives
## 1789-Washington 2
## 1793-Washington 0
## 1797-Adams 2
## 1801-Jefferson 0
docfreq(myDfm2)["representatives"]
## representatives
## 2
colSums(myDfm2)["representatives"]
## representatives
## 4
更新：quanteda v0.9.9中的某些命令和行为已更改：
返回一个简单的矢量，保留V形符号：
as.character(toks <- tokens(text, ngrams = 3, what = "fasterword"))
# [1] "<s>I'm_a_sentence" "a_sentence_and" "sentence_and_I'd"
# [4] "and_I'd_better" "I'd_better_be" "better_be_formatted"
# [7] "be_formatted_properly!</s><s>I'm" "formatted_properly!</s><s>I'm_a" "properly!</s><s>I'm_a_second"
# [10] "a_second_sentence</s>"
保持在句子内：
(sents <- as.character(tokens(text, what = "sentence")))
# [1] "<s>I'm a sentence and I'd better be formatted properly!" "</s><s>I'm a second sentence</s>"
tokens(sents, ngrams = 3, what = "fasterword")
# tokens from 2 documents.
# Component 1 :
# [1] "<s>I'm_a_sentence" "a_sentence_and" "sentence_and_I'd" "and_I'd_better" "I'd_better_be"
# [6] "better_be_formatted" "be_formatted_properly!"
#
# Component 2 :
# [1] "</s><s>I'm_a_second" "a_second_sentence</s>"
奖金问题第1部分：
featnames(dfm(text, verbose = FALSE, what = "fasterword", removePunct = FALSE))
# [1] "<s>i'm" "a" "sentence" "and" "i'd"
# [6] "better" "be" "formatted" "properly!</s><s>i'm" "second"
# [11] "sentence</s>"
红利问题第2部分没有变化。
答案 1 :(得分：1)
这样的方法怎么样：
ngrams(
tokenize(
unlist(
segment(text, what = "other", delimiter = "(?<=\\</s\\>)", perl = TRUE)),
what = "fastestword", simplify = TRUE),
n = 3L)
# [1] "<s>I'm_a_sentence" "a_sentence_and"
# [3] "sentence_and_I'd" "and_I'd_better"
# [5] "I'd_better_be" "better_be_formatted"
# [7] "be_formatted_properly!</s>" "formatted_properly!</s>_<s>I'm"
# [9] "properly!</s>_<s>I'm_a" "<s>I'm_a_second"
#[11] "a_second_sentence</s>"
或者，如果你不想要跨越句子界限的ngram：
unlist(
ngrams(
tokenize(
unlist(
segment(text, what = "other", delimiter = "(?<=\\</s\\>)", perl = TRUE)),
what = "fastestword"),
n = 3L))
#[1] "<s>I'm_a_sentence" "a_sentence_and"
#[3] "sentence_and_I'd" "and_I'd_better"
#[5] "I'd_better_be" "better_be_formatted"
#[7] "be_formatted_properly!</s>" "<s>I'm_a_second"
#[9] "a_second_sentence</s>"
我将自定义选项（例如removePunct = TRUE等）留给您。