Typical use cases include:
- Combining various third-party NLP and Machine Learning (ML) tools into one setup.
- Implementing algorithms that combine output from various NLP or ML modules and return one coherent analysis—also if their output is contradictory.
- Implementing dictionary-based, ontology-based or linguistically motivated approaches to NLP.
- Extraction of features from low-level NLP tools (tokenizers, taggers) for downstream ML models.
- Implementing white- or backlists that often sit on top of ML approaches in order to deal with specific mistakes of the ML models.