decipher.RdCurrently 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) # }