natural-language

# 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.

**Scope boundary:** Use this skill after you already have text. It owns
tokenization, language identification, POS/NER tagging, sentiment, embeddings,
custom `NLModel` classifiers/taggers, and in-app translation. Hand off OCR to
`vision-framework`, speech-to-text to `speech-recognition`, UI strings and
locale formatting to `ios-localization`, and generative summarization or Apple
Intelligence workflows to `apple-on-device-ai`.

## Contents

- [Setup](#setup)
- [Tokenization](#tokenization)
- [Language Identification](#language-identification)
- [Part-of-Speech Tagging](#part-of-speech-tagging)
- [Named Entity Recognition](#named-entity-recognition)
- [Sentiment Analysis](#sentiment-analysis)
- [Text Embeddings](#text-embeddings)
- [Translation](#translation)
- [Common Mistakes](#common-mistakes)
- [Review Checklist](#review-checklist)
- [References](#references)

## Setup

Import `NaturalLanguage` for text analysis and `Translation` for language
translation. No special entitlements or capabilities are required for
NaturalLanguage. Translation has split availability: system translation
presentation is iOS 17.4+ / macOS 14.4+, while `TranslationSession`,
`.translationTask()`, `LanguageAvailability`, and batch translation require
iOS 18+ / macOS 15+.
Direct `TranslationSession(installedSource:target:)` is the non-UI option, but
only when the source and target languages are already installed on device.

```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 { Do