WWDC 2017
Session video and resources: https://developer.apple.com/videos/play/wwdc2017/208/
- Typed, recognized handwriting, transcribed speech
- Natural language text -> Language identification
- Natural language text -> Tokenization -> Tokenize text
- Natural language text -> Part of speech -> Assign parts of speech
- Natural language text -> Lemmatization -> Lemmatize text
- Natural language text -> Named entity recognition
- Used for segmenting and tagging text
- Omitting whitespaces and punctuation is also possible
- Pass in schemas for options like lemmatization, language identification etc.
public enum NSLinguisticTaggerUnit
- word
- sentence
- paragraph
- document
dominantLanguage
- Improved performance
- Higher accuracy
- Additional language support
- A simple photo app with tagged descriptions
- A query for an example word like
hike
will return related results via NLP. Rather than just searching for the exact wordhike
- Recognizes dominant language and analyses through lemmatization
- A query for an example word like
- A social media app merging all major social media providers
- Organize feeds by people, organization and location using NLP
- Homogeneous text processing
- Consistent UX and results across all platforms and devices
- Privacy
- On-device machine learning
- Performance
- Multi-threaded
- 50K tokens/sec in a single thread, 80K tokens/sec in a multi-thread
- Language support
- 29 scripts, 52 languages for language identification
- Tokenization for all iOS/macOS languages