NLPLemmatization.Swift

import UIKit

let inputString = "Delhi, India’s capital territory, is a massive metropolitan area in the country’s north. In Old Delhi, a neighborhood dating to the 1600s, stands the imposing Mughal-era Red Fort, a symbol of India, and the sprawling Jama Masjid mosque, whose courtyard accommodates 25,000 people. Nearby is Chandni Chowk, a vibrant bazaar filled with food carts, sweets shops and spice stalls."

// tag schemes: tag schemes are constants that are used to identify pieces of information that we want from the input text. Tag schemes asks tagger to look for informations like
// Token type: a contant to classify each character as a word, punctuation or a whitespace
// Language: a constant to determine langugage of the token
// LexicalClass: this constant determines class of each token. i.e. it determines part of speech for a word, type of punctuation for a punctuation or type of whitespace for a whitespace
// Name type: this constant looks for tokens that are part of a named entity. It will look for a person's name , organizational name and name of a place
// Lemma: this constant returns the stem of word.
let tagger = NSLinguisticTagger(tagSchemes: [NSLinguisticTagScheme.tokenType, .language, .lexicalClass, .nameType, .lemma], options: 0)

// Options are the way to tell API as how to split the text. We are asking to ignore any punctuations and any whitespaces. Also, if there is a named entity then join it together i.e instead of considering "New" "Delhi" as two entities, join them together as one which is "New Delhi"

let options: NSLinguisticTagger.Options = [NSLinguisticTagger.Options.omitPunctuation, .omitWhitespace, .joinNames]

// ******************************************
// Lemmatization

// Lemmatization is the process of breaking down word into its most basic form. For example, "go" can be transformed into "gone", "will go", "went" etc and since therere are so many forms for NLP to understand it better the words are converted into their base root, like in this case all the above forms transform to "go" and this "go" is known as Lemma

func lemmatizeString() {
    tagger.string = inputString
    
    let range = NSRange(location: 0, length: inputString.utf16.count)
    
    tagger.enumerateTags(in: range, unit: NSLinguisticTaggerUnit.word, scheme: NSLinguisticTagScheme.lemma, options: options) { (tag, tokenRange, _) in
        if let lemma = tag?.rawValue {
            print(lemma)
        }
    }
}

lemmatizeString()