natural-language

SKILL.md

NaturalLanguage + Translation

Analyze natural language text for tokenization, part-of-speech tagging, named
entity recognition, sentiment analysis, language identification, and word/sentence
embeddings. Translate text between languages with the Translation framework.
Targets Swift 6.3 / iOS 26+.

This skill covers two related frameworks: NaturalLanguage (NLTokenizer, NLTagger, NLEmbedding) for on-device text analysis, and Translation (TranslationSession, LanguageAvailability) for language translation.

Contents

Setup

Tokenization

Language Identification

Part-of-Speech Tagging

Named Entity Recognition

Sentiment Analysis

Text Embeddings

Translation

Common Mistakes

Review Checklist

References

Setup

Import NaturalLanguage for text analysis and Translation for language
translation. No special entitlements or capabilities are required for
NaturalLanguage. Translation requires iOS 17.4+ / macOS 14.4+.

import NaturalLanguage
import Translation

NaturalLanguage classes (NLTokenizer, NLTagger) are not thread-safe.
Use each instance from one thread or dispatch queue at a time.

Tokenization

Segment text into words, sentences, or paragraphs with NLTokenizer.

import NaturalLanguage

func tokenizeWords(in text: String) -> [String] {
    let tokenizer = NLTokenizer(unit: .word)
    tokenizer.string = text

    let range = text.startIndex..<text.endIndex
    return tokenizer.tokens(for: range).map { String(text[$0]) }
}

Token Units

Unit
Description

.word
Individual words

.sentence
Sentences

.paragraph
Paragraphs

.document
Entire document

Enumerating with Attributes

Use enumerateTokens(in:using:) to detect numeric or emoji tokens.

let tokenizer = NLTokenizer(unit: .word)
tokenizer.string = text

tokenizer.enumerateTokens(in: text.startIndex..<text.endIndex) { range, attributes in
    if attributes.contains(.numeric) {
        print("Number: \(text[range])")
    }
    return true // continue enumeration
}

Language Identification

Detect the dominant language of a string with NLLanguageRecognizer.

func detectLanguage(for text: String) -> NLLanguage? {
    NLLanguageRecognizer.dominantLanguage(for: text)
}

// Multiple hypotheses with confidence scores
func languageHypotheses(for text: String, max: Int = 5) -> [NLLanguage: Double] {
    let recognizer = NLLanguageRecognizer()
    recognizer.processString(text)
    return recognizer.languageHypotheses(withMaximum: max)
}

Constrain the recognizer to expected languages for better accuracy on short text.

let recognizer = NLLanguageRecognizer()
recognizer.languageConstraints = [.english, .french, .spanish]
recognizer.processString(text)
let detected = recognizer.dominantLanguage

Part-of-Speech Tagging

Identify nouns, verbs, adjectives, and other lexical classes with NLTagger.

func tagPartsOfSpeech(in text: String) -> [(String, NLTag)] {
    let tagger = NLTagger(tagSchemes: [.lexicalClass])
    tagger.string = text

    var results: [(String, NLTag)] = []
    let range = text.startIndex..<text.endIndex
    let options: NLTagger.Options = [.omitPunctuation, .omitWhitespace]

    tagger.enumerateTags(in: range, unit: .word, scheme: .lexicalClass, options: options) { tag, tokenRange in
        if let tag {
            results.append((String(text[tokenRange]), tag))
        }
        return true
    }
    return results
}

Common Tag Schemes

Scheme
Output

.lexicalClass
Part of speech (noun, verb, adjective)

.nameType
Named entity type (person, place, organization)

.nameTypeOrLexicalClass
Combined NER + POS

.lemma
Base form of a word

.language
Per-token language

.sentimentScore
Sentiment polarity score

Named Entity Recognition

Extract people, places, and organizations.

func extractEntities(from text: String) -> [(String, NLTag)] {
    let tagger = NLTagger(tagSchemes: [.nameType])
    tagger.string = text

    var entities: [(String, NLTag)] = []
    let options: NLTagger.Options = [.omitPunctuation, .omitWhitespace, .joinNames]

    tagger.enumerateTags(
        in: text.startIndex..<text.endIndex,
        unit: .word,
        scheme: .nameType,
        options: options
    ) { tag, tokenRange in
        if let tag, tag != .other {
            entities.append((String(text[tokenRange]), tag))
        }
        return true
    }
    return entities
}
// NLTag values: .personalName, .placeName, .organizationName

Sentiment Analysis

Score text sentiment from -1.0 (negative) to +1.0 (positive).

func sentimentScore(for text: String) -> Double? {
    let tagger = NLTagger(tagSchemes: [.sentimentScore])
    tagger.string = text

    let (tag, _) = tagger.tag(
        at: text.startIndex,
        unit: .paragraph,
        scheme: .sentimentScore
    )
    return tag.flatMap { Double($0.rawValue) }
}

Text Embeddings

Measure semantic similarity between words or sentences with NLEmbedding.

func wordSimilarity(_ word1: String, _ word2: String) -> Double? {
    guard let embedding = NLEmbedding.wordEmbedding(for: .english) else { return nil }
    return embedding.distance(between: word1, and: word2, distanceType: .cosine)
}

func findSimilarWords(to word: String, count: Int = 5) -> [(String, Double)] {
    guard let embedding = NLEmbedding.wordEmbedding(for: .english) else { return [] }
    return embedding.neighbors(for: word, maximumCount: count, distanceType: .cosine)
}

Sentence embeddings compare entire sentences.

func sentenceSimilarity(_ s1: String, _ s2: String) -> Double? {
    guard let embedding = NLEmbedding.sentenceEmbedding(for: .english) else { return nil }
    return embedding.distance(between: s1, and: s2, distanceType: .cosine)
}

Translation

System Translation Overlay

Show the built-in translation UI with .translationPresentation().

import SwiftUI
import Translation

struct TranslatableView: View {
    @State private var showTranslation = false
    let text = "Hello, how are you?"

    var body: some View {
        Button { showTranslation = true } label: {
            Text(text)
        }
        .buttonStyle(.plain)
        .translationPresentation(
            isPresented: $showTranslation,
            text: text
        )
    }
}

Programmatic Translation

Use .translationTask() for programmatic translations within a view context.

struct TranslatingView: View {
    @State private var translatedText = ""
    @State private var configuration: TranslationSession.Configuration?

    var body: some View {
        VStack {
            Text(translatedText)
            Button("Translate") {
                configuration = .init(source: Locale.Language(identifier: "en"),
                                      target: Locale.Language(identifier: "es"))
            }
        }
        .translationTask(configuration) { session in
            let response = try await session.translate("Hello, world!")
            translatedText = response.targetText
        }
    }
}

Batch Translation

Translate multiple strings in a single session.

.translationTask(configuration) { session in
    let requests = texts.enumerated().map { index, text in
        TranslationSession.Request(sourceText: text,
                                    clientIdentifier: "\(index)")
    }
    let responses = try await session.translations(from: requests)
    for response in responses {
        print("\(response.sourceText) -> \(response.targetText)")
    }
}

Checking Language Availability

let availability = LanguageAvailability()
let status = await availability.status(
    from: Locale.Language(identifier: "en"),
    to: Locale.Language(identifier: "ja")
)
switch status {
case .installed: break    // Ready to translate offline
case .supported: break    // Needs download
case .unsupported: break  // Language pair not available
}

Common Mistakes

DON'T: Share NLTagger/NLTokenizer across threads

These classes are not thread-safe and will produce incorrect results or crash.

// WRONG
let sharedTagger = NLTagger(tagSchemes: [.lexicalClass])
DispatchQueue.concurrentPerform(iterations: 10) { _ in
    sharedTagger.string = someText  // Data race
}

// CORRECT
await withTaskGroup(of: Void.self) { group in
    for _ in 0..<10 {
        group.addTask {
            let tagger = NLTagger(tagSchemes: [.lexicalClass])
            tagger.string = someText
            // process...
        }
    }
}

DON'T: Confuse NaturalLanguage with Core ML

NaturalLanguage provides built-in linguistic analysis. Use Core ML for custom
trained models. They complement each other via NLModel.

// WRONG: Trying to do NER with raw Core ML
let coreMLModel = try MLModel(contentsOf: modelURL)

// CORRECT: Use NLTagger for built-in NER
let tagger = NLTagger(tagSchemes: [.nameType])

// Or load a custom Core ML model via NLModel
let nlModel = try NLModel(mlModel: coreMLModel)
tagger.setModels([nlModel], forTagScheme: .nameType)

DON'T: Assume embeddings exist for all languages

Not all languages have word or sentence embeddings available on device.

// WRONG: Force unwrap
let embedding = NLEmbedding.wordEmbedding(for: .japanese)!

// CORRECT: Handle nil
guard let embedding = NLEmbedding.wordEmbedding(for: .japanese) else {
    // Embedding not available for this language
    return
}

DON'T: Create a new tagger per token

Creating and configuring a tagger is expensive. Reuse it for the same text.

// WRONG: New tagger per word
for word in words {
    let tagger = NLTagger(tagSchemes: [.lexicalClass])
    tagger.string = word
}

// CORRECT: Set string once, enumerate
let tagger = NLTagger(tagSchemes: [.lexicalClass])
tagger.string = fullText
tagger.enumerateTags(in: fullText.startIndex..<fullText.endIndex,
                     unit: .word, scheme: .lexicalClass, options: []) { tag, range in
    return true
}

DON'T: Ignore language hints for short text

Language detection on short strings (under ~20 characters) is unreliable.
Set constraints or hints to improve accuracy.

// WRONG: Detect language of a single word
let lang = NLLanguageRecognizer.dominantLanguage(for: "chat")  // French or English?

// CORRECT: Provide context
let recognizer = NLLanguageRecognizer()
recognizer.languageHints = [.english: 0.8, .french: 0.2]
recognizer.processString("chat")

Review Checklist

 NLTokenizer and NLTagger instances used from a single thread

 Tagger created once per text, not per token

 Language detection uses constraints/hints for short text

 NLEmbedding availability checked before use (returns nil if unavailable)

 Translation LanguageAvailability checked before attempting translation

 .translationTask() used within a SwiftUI view hierarchy

 Batch translation uses clientIdentifier to match responses to requests

 Sentiment scores handled as optional (may return nil for unsupported languages)

 .joinNames option used with NER to keep multi-word names together

 Custom ML models loaded via NLModel, not raw Core ML

References

Extended patterns (custom models, contextual embeddings, gazetteers): references/translation-patterns.md

Natural Language framework

NLTokenizer

NLTagger

NLEmbedding

NLLanguageRecognizer

Translation framework

TranslationSession

LanguageAvailability