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 6.2 / iOS 26+.

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

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+.

swift

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

swift

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.

swift

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

swift

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.

swift

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

swift

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.

swift

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).

swift

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

swift

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.

swift

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()

swift

import SwiftUI
import Translation

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

    var body: some View {
        Text(text)
            .onTapGesture { showTranslation = true }
            .translationPresentation(
                isPresented: $showTranslation,
                text: text
            )
    }
}

Programmatic Translation

Use

.translationTask()

for programmatic translations within a view context.

swift

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.

swift

.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

swift

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.

swift

// 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

swift

// 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.

swift

// 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.

swift

// 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.

swift

// 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

natural-language

NPX Install

Tags

SKILL.md Content

NaturalLanguage + Translation

Contents

Setup

Tokenization

Token Units

Enumerating with Attributes

Language Identification

Part-of-Speech Tagging

Common Tag Schemes

Named Entity Recognition

Sentiment Analysis

Text Embeddings

Translation

System Translation Overlay

Programmatic Translation

Batch Translation

Checking Language Availability

Common Mistakes

DON'T: Share NLTagger/NLTokenizer across threads

DON'T: Confuse NaturalLanguage with Core ML

DON'T: Assume embeddings exist for all languages

DON'T: Create a new tagger per token

DON'T: Ignore language hints for short text

Review Checklist

References