R разорвать корпус в предложения

Ответ 1

Я не знаю, как изменить корпус, но это будет фантастическая функциональность.

Я предполагаю, что мой подход будет примерно таким:

Использование этих пакетов

# Load Packages
require(tm)
require(NLP)
require(openNLP)

Я бы настроил свой текст на функции предложений следующим образом:

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)
}

И мой взлом функции reshape corpus (NB: вы потеряете мета атрибуты здесь, если вы каким-либо образом не измените эту функцию и не скопируете их соответствующим образом)

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)
}

Что работает следующим образом:

## 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 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

Мой вывод sessionInfo

> 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  

Ответ 2

openNLP произошли некоторые серьезные изменения. Плохая новость - это выглядит совсем не так, как раньше. Хорошей новостью является то, что она более гибкая, и функциональность, которой вы пользовались раньше, все еще существует, вам просто нужно ее найти.

Это даст вам то, что вам нужно:

?Maxent_Sent_Token_Annotator

Просто выполните этот пример, и вы увидите функциональность, которую вы ищете.

Ответ 3

Просто преобразуйте ваш корпус в фреймворк данных и используйте регулярные выражения для обнаружения предложений.

Вот функция, которая использует регулярные выражения для обнаружения предложений в абзаце и возвращает каждое отдельное предложение.

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)
    }

... Использование одного абзаца внутри корпуса из пакета tm.

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)

Используйте следующее:

chunk_into_sentences(corpus_frame)

Что дает нам:

[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."

Теперь с большим корпусом

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))

Используйте следующее:

lapply(my_big_corpus, chunk_into_sentences)

Что дает нам:

$`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." 

Ответ 4

С qdap version 1.1.0 вы можете выполнить это со следующим (я использовал набор данных @Tony Breyal current.corpus):

library(qdap)
with(sentSplit(tm_corpus2df(current.corpus), "text"), df2tm_corpus(tot, text))

Вы также можете сделать:

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 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

Ответ 5

Ошибка связана с пакетом ggplot2, и функция аннотации дает эту ошибку, отсоединяет пакет ggplot2, а затем повторяет попытку. Надеюсь, это сработает.

Ответ 6

Я применил следующий код для решения одной и той же проблемы с помощью пакета tokenizers.

# 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))

Ответ 7

Это функция, построенная на этом решении Python, что позволяет использовать некоторую гибкость в том, что списки префиксов, суффиксов и т.д. Могут быть изменены для вашего конкретного текста. Это определенно не идеально, но может быть полезно с правильным текстом.

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.