import Parsimmon

let tokenizer = Tokenizer() let tokens = tokenizer.tokenize("The quick brown fox jumps over the lazy dog") print(tokens)

let tagger = Tagger() let taggedTokens = tagger.tagWordsInText("The quick brown fox jumps over the lazy dog") print(taggedTokens)

let lemmatizer = Lemmatizer() let lemmatizedTokens = lemmatizer.lemmatizeWordsInText("Diane, I'm holding in my hand a small box of chocolate bunnies.") print(lemmatizedTokens)

let classifier = NaiveBayesClassifier() // Train the classifier with some ham examples. classifier.trainWithText("nom nom ham", category: "ham") classifier.trainWithText("make sure to get the ham", category: "ham") classifier.trainWithText("please put the eggs in the fridge", category: "ham") // Train the classifier with some spam examples. classifier.trainWithText("spammy spam spam", category: "spam") classifier.trainWithText("what does the fox say?", category: "spam") classifier.trainWithText("and fish go blub", category: "spam") // Classify some new text. Is it ham or spam? // In practice, you'd want to train with more examples first. let firstExample = "use the eggs in the fridge." let secondExample = "what does the fish say?" print("\(firstExample) => \(classifier.classify(firstExample))") print("\(secondExample) => \(classifier.classify(secondExample))")