Tokenize, tag, and analyze natural language text using Apple's NaturalLanguage framework and translate between languages with the Translation framework. Use when adding language identification, sentiment analysis, named entity recognition, part-of-speech tagging, text embeddings, or in-app translati

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. 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. 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 . 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. Language Identification Detect the dominant language of a string with NLLanguageRecognizer . Constrain the recognizer to expected languages for better accuracy on short text. Part of Speech Tagging Identify nouns, verbs, adjectives, and other lexical classes with NLTagger . 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. Sentiment Analysis Score text sentiment from 1.0 (negative) to +1.0 (positive). Text Embeddings Measure semantic similarity between words or sentences with NLEmbedding . Sentence embeddings compare entire sentences. Translation System Translation Overlay Show the built in translation UI with .translationPresentation() . Programmatic Translation Use .translationTask() for programmatic translations within a view context. Batch Translation Translate multiple strings in a single session. Checking Language Availability Common Mistakes DON'T: Share NLTagger/NLTokenizer across threads These classes are not thread safe and will produce incorrect results or crash. 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 . DON'T: Assume embeddings exist for all languages Not all languages have word or sentence embeddings available on device. DON'T: Create a new tagger per token Creating and configuring a tagger is expensive. Reuse it for the same text. 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. 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](references/translation patterns.md) [Natural Language framework](https://sosumi.ai/documentation/naturallanguage) [NLTokenizer](https://sosumi.ai/documentation/naturallanguage/nltokenizer) [NLTagger](https://sosumi.ai/documentation/naturallanguage/nltagger) [NLEmbedding](https://sosumi.ai/documentation/naturallanguage/nlembedding) [NLLanguageRecognizer](https://sosumi.ai/documentation/naturallanguage/nllanguagerecognizer) [Translation framework](https://sosumi.ai/documentation/translation) [TranslationSession](https://sosumi.ai/documentation/translation/translationsession) [TranslationSession.Strategy](https://sosumi.ai/documentation/translation/translationsession/strategy) [LanguageAvailability](https://sosumi.ai/documentation/translation/languageavailability)

natural-language