Peter Norvig’s Spell Checker in Two Lines of Base R

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Peter Norvig, the director of research at Google, wrote a nice essay on How to Write a Spelling Corrector a couple of years ago. That essay explains and implements a simple but effective spelling correction function in just 21 lines of Python. Highly recommended reading! I was wondering how many lines it would take to write something similar in base R. Turns out you can do it in (at least) two pretty obfuscated lines:

sorted_words <- names(sort(table(strsplit(tolower(paste(readLines(""), collapse = " ")), "[^a-z]+")), decreasing = TRUE))
correct <- function(word) { c(sorted_words[ adist(word, sorted_words) <= min(adist(word, sorted_words), 2)], word)[1] }

While not working exactly as Norvig’s version it should result in similar spelling corrections:

## [1] “piece”
## [1] “of”
correct(“cakke”) ## [1] “cake”

So let’s deobfuscate the two-liner slightly (however, the code below might not make sense if you don’t read Norvig’s essay first):

# Read in big.txt, a 6.5 mg collection of different English texts.
raw_text <- paste(readLines(""), collapse = " ")
# Make the text lowercase and split it up creating a huge vector of word tokens.
split_text <- strsplit(tolower(raw_text), "[^a-z]+")
# Count the number of different type of words.
word_count <- table(split_text)
# Sort the words and create an ordered vector with the most common type of words first.
sorted_words <- names(sort(word_count, decreasing = TRUE))

correct <- function(word) {
  # Calculate the edit distance between the word and all other words in sorted_words.
  edit_dist <- adist(word, sorted_words)
  # Calculate the minimum edit distance to find a word that exists in big.txt 
  # with a limit of two edits.
  min_edit_dist <- min(edit_dist, 2)
  # Generate a vector with all words with this minimum edit distance.
  # Since sorted_words is ordered from most common to least common, the resulting
  # vector will have the most common / probable match first.
  proposals_by_prob <- c(sorted_words[ edit_dist <= min(edit_dist, 2)])
  # In case proposals_by_prob would be empty we append the word to be corrected...
  proposals_by_prob <- c(proposals_by_prob, word)
  # ... and return the first / most probable word in the vector.

Some thoughts:

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