elinor for Data Teams
Domain-driven natural language processing for better results.
Understanding your domain: No matter if you want to classify documents, find mentions of entities in texts or disambiguate entity candidates. Explicitly stating hierarchies, concept relations, and synonyms help you understand what you want to model. Encoding your domain knowledge in an ontology creates transparency and clarity.
Making predictions interoperable: Using explicit data schemas, you can use predictions from your trained model in other contexts and combine them with other datasets. Ontologies are nothing new (not by a long shot), but linked data is having a revival - for a good reason.
Interpretable and smaller models: By infusing your domain knowledge into text transformer models (e.g., with siamese neural networks), your models can be considerably smaller than full-blown transfer-learned foundational models. Your results will be as accurate or even better - while being easier to interpret.
- Linked Open Data with ontologies
- Named Entity Recognition
- Span Classification
- Text Classification
- Entity Linking and Entity Disambiguation