- Natural Language Generation Systems
- Integrating Deep and Shallow Natural Language Processing Components - Representations and Hybrid Architectures
- In-Depth Generation vs. Shallow Generation
- SIGGEN: Who's who in NLG
- Natural Language Generation Group at the Department of Computing Science, University of Aberdeen (NLG Group Aberdeen)
- University of Aberdeen
- Language Technology Lab (LT Lab)
- Special Interest Group on Generation (SIGGEN)
- Ehud Reiter
- Stephan Busemann
- Transnational Environmental Management Support & Information System (TEMSIS)
- Smoking Termination with cOmputerised Personalisation (STOP)
Shallow generation is an approach to a domain and task specific approach to natural language generation (NLG).
Shallow generation corresponds to shallow analysis in an interesting way. Whereas the latter ignores information by yielding incomplete analysis, the former adds information with help of output text not based on the input representation. For instance, in the domain of air quality measurements, the input " location: 'St. Avold' " may give rise to the output "at the measuring station of St. Avold", where "measuring station" is domain-specific.
Time-efficient shallow approaches to small applications can be very quickly developed by defining domain-specific grammars. Good examples are report generators (stock market, weather, air quality, limited dialogue, or NL access to database contents. They are less recommendable when the application requires a large variety of outputs and consequently a large linguistic knowledge base.
Note that the distinction between shallow and deep generation is gradual rather than bipolar.