Joint NER and Entity Linking Model for Noisy Social Media Text

The patent merges the previously separate NER and EL models into a single unified joint model. This joint model simultaneously performs both named entity recognition and entity linking tasks, allowing the system to leverage dependencies between the two tasks. By combining the models, errors no longer propagate sequentially from NER to EL, thereby improving recall and accuracy while maintaining a manageable system structure through shared parameters and coordinated training.

The joint unified model achieves multi-functionality by simultaneously performing both NER and EL tasks within a single model framework. The model uses shared parameters and features that serve both entity recognition and entity linking purposes. This universal approach allows the model to leverage dependencies between tasks, improving recall and accuracy while managing complexity through parameter sharing and coordinated optimization rather than fully independent sub-models.

The patent combines separate training processes into a unified joint training framework. Instead of training NER and EL models separately in a pipeline fashion, the joint model is trained simultaneously on both tasks using coordinated optimization. This unified training approach allows gradient updates to reflect dependencies between NER and EL objectives, preventing error propagation that occurs when models are trained and applied sequentially in traditional pipeline architectures.