Rコーパスを文に分割
ライブラリ
tmを使用してコーパスに読み込んだPDFドキュメントがいくつかあります。コーパスを文に分割するにはどうすればよいですか？これは、パッケージ
readLines[*]からsentSplitの後にqdapを付けてファイルを読み取ることで実行できます。その関数にはデータフレームが必要です。また、コーパスを破棄して、すべてのファイルを個別に読み取る必要があります。関数
sentSplit{qdap}をtmのコーパスに渡すにはどうすればよいですか？それとももっと良い方法はありますか？.
注：ライブラリsentDetectに関数openNLPがありましたが、現在はMaxent_Sent_Token_Annotator-同じ質問が当てはまります：これをコーパス[tm]とどのように組み合わせることができますか？
コーパスの形を変える方法はわかりませんが、それは素晴らしい機能です。
私のアプローチは次のようなものになると思います。
これらのパッケージの使用
# Load Packages
require(tm)
require(NLP)
require(openNLP)
テキストから文への機能を次のように設定します。
convert_text_to_sentences <- function(text, lang = "en") {
# Function to compute sentence annotations using the Apache OpenNLP Maxent sentence detector employing the default model for language 'en'.
sentence_token_annotator <- Maxent_Sent_Token_Annotator(language = lang)
# Convert text to class String from package NLP
text <- as.String(text)
# Sentence boundaries in text
sentence.boundaries <- annotate(text, sentence_token_annotator)
# Extract sentences
sentences <- text[sentence.boundaries]
# return sentences
return(sentences)
}
そして、コーパス関数の形状変更の私のハック（注：この関数を何らかの方法で変更して適切にコピーしない限り、ここでメタ属性が失われます）
reshape_corpus <- function(current.corpus, FUN, ...) {
# Extract the text from each document in the corpus and put into a list
text <- lapply(current.corpus, Content)
# Basically convert the text
docs <- lapply(text, FUN, ...)
docs <- as.vector(unlist(docs))
# Create a new corpus structure and return it
new.corpus <- Corpus(VectorSource(docs))
return(new.corpus)
}
これは次のように機能します。
## create a corpus
dat <- data.frame(doc1 = "Doctor Who is a British science fiction television programme produced by the BBC. The programme depicts the adventures of a Time Lord—a time travelling, humanoid alien known as the Doctor. He explores the universe in his TARDIS (acronym: Time and Relative Dimension in Space), a sentient time-travelling space ship. Its exterior appears as a blue British police box, a common sight in Britain in 1963, when the series first aired. Along with a succession of companions, the Doctor faces a variety of foes while working to save civilisations, help ordinary people, and right wrongs.",
doc2 = "The show has received recognition from critics and the public as one of the finest British television programmes, winning the 2006 British Academy Television Award for Best Drama Series and five consecutive (2005–10) awards at the National Television Awards during Russell T Davies's tenure as Executive Producer.[3][4] In 2011, Matt Smith became the first Doctor to be nominated for a BAFTA Television Award for Best Actor. In 2013, the Peabody Awards honoured Doctor Who with an Institutional Peabody \"for evolving with technology and the times like nothing else in the known television universe.\"[5]",
doc3 = "The programme is listed in Guinness World Records as the longest-running science fiction television show in the world[6] and as the \"most successful\" science fiction series of all time—based on its over-all broadcast ratings, DVD and book sales, and iTunes traffic.[7] During its original run, it was recognised for its imaginative stories, creative low-budget special effects, and pioneering use of electronic music (originally produced by the BBC Radiophonic Workshop).",
stringsAsFactors = FALSE)
current.corpus <- Corpus(VectorSource(dat))
# A corpus with 3 text documents
## reshape the corpus into sentences (modify this function if you want to keep meta data)
reshape_corpus(current.corpus, convert_text_to_sentences)
# A corpus with 10 text documents
私のsessionInfo出力
> sessionInfo()
R version 3.0.1 (2013-05-16)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United Kingdom.1252 LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] NLP_0.1-0 openNLP_0.2-1 tm_0.5-9.1
loaded via a namespace (and not attached):
[1] openNLPdata_1.5.3-1 parallel_3.0.1 rJava_0.9-4 slam_0.1-29 tools_3.0.1
openNLPにはいくつかの大きな変更がありました。悪いニュースは、以前とは非常に異なって見えることです。良いニュースは、それがより柔軟であり、以前に楽しんだ機能がまだそこにあるということです、あなたはそれを見つける必要があります。
これはあなたが求めているものをあなたに与えるでしょう：
?Maxent_Sent_Token_Annotator
例を実行するだけで、探している機能が表示されます。
コーパスをデータフレームに変換し、正規表現を使用して文を検出するだけです。
これは、正規表現を使用して段落内の文を検出し、個々の文を返す関数です。
chunk_into_sentences <- function(text) {
break_points <- c(1, as.numeric(gregexpr('[[:alnum:] ][.!?]', text)[[1]]) + 1)
sentences <- NULL
for(i in 1:length(break_points)) {
res <- substr(text, break_points[i], break_points[i+1])
if(i>1) { sentences[i] <- sub('. ', '', res) } else { sentences[i] <- res }
}
sentences <- sentences[sentences=!is.na(sentences)]
return(sentences)
}
... tmパッケージのコーパス内の1つの段落を使用します。
text <- paste('Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.')
mycorpus <- VCorpus(VectorSource(text))
corpus_frame <- data.frame(text=unlist(sapply(mycorpus, `[`, "content")), stringsAsFactors=F)
次のように使用します：
chunk_into_sentences(corpus_frame)
それは私たちに与えます：
[1] "Lorem Ipsum is simply dummy text of the printing and typesetting industry."
[2] "Lorem Ipsum has been the industry standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book."
[3] "It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged."
[4] "It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum."
コーパスが大きくなりました
text1 <- "Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industrys standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum."
text2 <- "It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like)."
text3 <- "There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything embarrassing hidden in the middle of text. All the Lorem Ipsum generators on the Internet tend to repeat predefined chunks as necessary, making this the first true generator on the Internet. It uses a dictionary of over 200 Latin words, combined with a handful of model sentence structures, to generate Lorem Ipsum which looks reasonable. The generated Lorem Ipsum is therefore always free from repetition, injected humour, or non-characteristic words etc."
text_list <- list(text1, text2, text3)
my_big_corpus <- VCorpus(VectorSource(text_list))
次のように使用します：
lapply(my_big_corpus, chunk_into_sentences)
それは私たちに与えます：
$`1`
[1] "Lorem Ipsum is simply dummy text of the printing and typesetting industry."
[2] "Lorem Ipsum has been the industrys standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book."
[3] "It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged."
[4] "It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum."
$`2`
[1] "It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout."
[2] "The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English."
[3] "Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy."
$`3`
[1] "There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don't look even slightly believable."
[2] "If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything embarrassing hidden in the middle of text."
[3] "All the Lorem Ipsum generators on the Internet tend to repeat predefined chunks as necessary, making this the first true generator on the Internet."
[4] "It uses a dictionary of over 200 Latin words, combined with a handful of model sentence structures, to generate Lorem Ipsum which looks reasonable."
[5] "The generated Lorem Ipsum is therefore always free from repetition, injected humour, or non-characteristic words etc."
tokenizersパッケージを使用して同じ問題を解決するために、次のコードを実装しました。
# Iterate a list or vector of strings and split into sentences where there are
# periods or question marks
sentences = purrr::map(.x = textList, function(x) {
return(tokenizers::tokenize_sentences(x))
})
# The code above will return a list of character vectors so unlist
# to give you a character vector of all the sentences
sentences = unlist(sentences)
# Create a corpus from the sentences
corpus = VCorpus(VectorSource(sentences))
これは構築された関数です this Python solution プレフィックス、サフィックスなどのリストを特定のテキストに変更できるという柔軟性があります。間違いなく完璧ではありませんが、適切なテキストで役立つ可能性があります。
caps = "([A-Z])"
prefixes = "(Mr|St|Mrs|Ms|Dr|Prof|Capt|Cpt|Lt|Mt)\\."
suffixes = "(Inc|Ltd|Jr|Sr|Co)"
acronyms = "([A-Z][.][A-Z][.](?:[A-Z][.])?)"
starters = "(Mr|Mrs|Ms|Dr|He\\s|She\\s|It\\s|They\\s|Their\\s|Our\\s|We\\s|But\\s|However\\s|That\\s|This\\s|Wherever)"
websites = "\\.(com|edu|gov|io|me|net|org)"
digits = "([0-9])"
split_into_sentences <- function(text){
text = gsub("\n|\r\n"," ", text)
text = gsub(prefixes, "\\1<prd>", text)
text = gsub(websites, "<prd>\\1", text)
text = gsub('www\\.', "www<prd>", text)
text = gsub("Ph.D.","Ph<prd>D<prd>", text)
text = gsub(paste0("\\s", caps, "\\. "), " \\1<prd> ", text)
text = gsub(paste0(acronyms, " ", starters), "\\1<stop> \\2", text)
text = gsub(paste0(caps, "\\.", caps, "\\.", caps, "\\."), "\\1<prd>\\2<prd>\\3<prd>", text)
text = gsub(paste0(caps, "\\.", caps, "\\."), "\\1<prd>\\2<prd>", text)
text = gsub(paste0(" ", suffixes, "\\. ", starters), " \\1<stop> \\2", text)
text = gsub(paste0(" ", suffixes, "\\."), " \\1<prd>", text)
text = gsub(paste0(" ", caps, "\\."), " \\1<prd>",text)
text = gsub(paste0(digits, "\\.", digits), "\\1<prd>\\2", text)
text = gsub("...", "<prd><prd><prd>", text, fixed = TRUE)
text = gsub('\\.”', '”.', text)
text = gsub('\\."', '\".', text)
text = gsub('\\!"', '"!', text)
text = gsub('\\?"', '"?', text)
text = gsub('\\.', '.<stop>', text)
text = gsub('\\?', '?<stop>', text)
text = gsub('\\!', '!<stop>', text)
text = gsub('<prd>', '.', text)
sentence = strsplit(text, "<stop>\\s*")
return(sentence)
}
test_text <- 'Dr. John Johnson, Ph.D. worked for X.Y.Z. Inc. for 4.5 years. He earned $2.5 million when it sold! Now he works at www.website.com.'
sentences <- split_into_sentences(test_text)
names(sentences) <- 'sentence'
df_sentences <- dplyr::bind_rows(sentences)
df_sentences
# A tibble: 3 x 1
sentence
<chr>
1 Dr. John Johnson, Ph.D. worked for X.Y.Z. Inc. for 4.5 years.
2 He earned $2.5 million when it sold!
3 Now he works at www.website.com.
このエラーはggplot2パッケージに関連していることを意味し、アノテーション関数はこのエラーを出し、ggplot2パッケージをデタッチしてから再試行します。うまくいけば、それはうまくいくはずです。
qdapバージョン1.1. を使用すると、次の方法でこれを実現できます（@TonyBreyalのcurrent.corpusデータセットを使用しました）。
library(qdap)
with(sentSplit(tm_corpus2df(current.corpus), "text"), df2tm_corpus(tot, text))
次のこともできます。
tm_map(current.corpus, sent_detect)
## inspect(tm_map(current.corpus, sent_detect))
## A corpus with 3 text documents
##
## The metadata consists of 2 tag-value pairs and a data frame
## Available tags are:
## create_date creator
## Available variables in the data frame are:
## MetaID
##
## $doc1
## [1] Doctor Who is a British science fiction television programme produced by the BBC.
## [2] The programme depicts the adventures of a Time Lord—a time travelling, humanoid alien known as the Doctor.
## [3] He explores the universe in his TARDIS, a sentient time-travelling space ship.
## [4] Its exterior appears as a blue British police box, a common sight in Britain in 1963, when the series first aired.
## [5] Along with a succession of companions, the Doctor faces a variety of foes while working to save civilisations, help ordinary people, and right wrongs.
##
## $doc2
## [1] The show has received recognition from critics and the public as one of the finest British television programmes, winning the 2006 British Academy Television Award for Best Drama Series and five consecutive awards at the National Television Awards during Russell T Davies's tenure as Executive Producer.
## [2] In 2011, Matt Smith became the first Doctor to be nominated for a BAFTA Television Award for Best Actor.
## [3] In 2013, the Peabody Awards honoured Doctor Who with an Institutional Peabody for evolving with technology and the times like nothing else in the known television universe.
##
## $doc3
## [1] The programme is listed in Guinness World Records as the longest-running science fiction television show in the world and as the most successful science fiction series of all time—based on its over-all broadcast ratings, DVD and book sales, and iTunes traffic.
## [2] During its original run, it was recognised for its imaginative stor