decipher: foo: A package to process natural language.
In news-r/decipher: Natural Language Processing tools for R

Description functions Examples

Currently the package allows training and using name finder models.

tnf to use a model and extract names from character vector.
tnf_ to use a model and extract names from file.
tnf_train train a name finder model from character vector.
tnf_train_ train a name finder model from file.
get_names extract identified names from character vector.
get_names_ extract identified names from file.
dc classify documents from character vector.
dc_ classify document from on file.
dc_train train document classifer from file.

## Not run: 
# get working directory
# need to pass full path
wd <- getwd()

# Name extraction
# Training to find "WEF"
data <- paste("This organisation is called the <START:wef> World Economic Forum <END>",
  "It is often referred to as <START:wef> Davos <END> or the <START:wef> WEF <END>.")

# Save the above as file
write(data, file = "input.txt")

# Trains the model and returns the full path to the model
model <- tnf_train_(model = paste0(wd, "/wef.bin"), lang = "en",
  data = paste0(wd, "/input.txt"), type = "wef")

# Create sentences to test our model
sentences <- paste("This sentence mentions the World Economic Forum the annual meeting",
  "of which takes place in Davos. Note that the forum is often called the WEF.")

# Save sentences
write(data, file = "sentences.txt")

# Extract names
# Without specifying an output file the extracted names appear in the console
tnf(model = model, sentences = paste0(wd, "/sentences.txt"))

# returns path to output file
output <- tnf_(model = model, sentences = paste0(wd, "/sentences.txt"),
  output = paste0(wd, "/output.txt"))

# extract names
(names <- get_names(output))

# Classification
# create dummy data
data <- data.frame(class = c("Sport", "Business", "Sport", "Sport"),
  doc = c("Football, tennis, golf and, bowling and, score",
          "Marketing, Finance, Legal and, Administration",
          "Tennis, Ski, Golf and, gym and, match",
          "football, climbing and gym"))

# repeat data 50 times to have enough data
# Obviously do not do that in te real world
data <- do.call("rbind", replicate(50, data, simplify = FALSE))

# train model
model <- dc_train(model = paste0(wd, "/model.bin"), data = data, lang = "en")

# create documents to classify
documents <- data.frame(
  docs = c("This discusses golf which is a sport.",
           "This documents is about business administration.",
           "This is about people who do sport, go to the gym and play tennis.",
           "Some play tennis and work in Finance")
)

# classify documents
classified <- dc(model, documents)

## End(Not run)