Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
 

Complex Named Entity Recognition

Traditional named entity recognition (NER) models and datasets have focused on mentions of persons, locations, and organizations, gaining good performance in well-structured English texts. Additionally modern language models like BERT have further improved NER models to achieve new state-of-the-art results. However, more complex scenarios with domain-specific entity types, shorter texts and different languages reduce the performance of NER models.
In order to create novel models which are able to improve NER performance in these set-ups, language models and NER models need to be adapted to take advantage of pre-trained models and incomplete structured knowledge.

More information can be found here.