Pre-trained Language Model for NER Confidence Prediction

The patent applies pre-trained language models (such as BERT) that have been pre-trained on large corpora before being fine-tuned for specific NER tasks. This preliminary pre-training action enables the model to capture general language patterns and contextual relationships, significantly improving NER accuracy without requiring extensive task-specific data or complex architecture design from scratch.

The patent employs universal pre-trained language models that can be adapted to multiple NER tasks across different domains and languages. These models serve multiple functions: they can perform NER on various entity types (persons, organizations, locations), adapt to different domains (finance, healthcare, legal), and provide both entity recognition and confidence estimation, reducing the need for domain-specific model development.

The patent leverages pre-trained models that have already learned from vast amounts of unlabeled text data during pre-training. This preliminary learning action transfers general language understanding to the NER task, allowing the model to achieve high accuracy with minimal labeled training data, thus reducing annotation costs and data requirements.

The patent implements semi-supervised learning approaches where the model can leverage unlabeled data alongside small labeled datasets. The pre-trained model's internal representations and predictions serve as self-generated features that improve learning from limited labeled examples, reducing dependency on extensive manual annotation while maintaining high accuracy.

The patent uses universal pre-trained language models that can be quickly adapted to new domains through fine-tuning on domain-specific data. These models provide multi-functionality by serving as base models for various NER tasks across different domains (finance, healthcare, legal, etc.), enabling rapid deployment without extensive retraining or architecture modification.

The patent employs fine-tuning techniques where the pre-trained model's parameters are adjusted on smaller domain-specific datasets. This parameter adjustment process allows the model to adapt to new domains efficiently, capturing domain-specific language patterns and entities while maintaining the general language understanding learned during pre-training, thus achieving fast adaptation with minimal training time.

The patent applies pre-trained models that have already learned from large corpora, transferring this knowledge to small document corpora. This preliminary learning action enables the model to maintain high NER accuracy even when processing small datasets, as the pre-trained representations provide robust features that generalize well to limited data scenarios.

The patent implements approaches where the model leverages its pre-trained knowledge to compensate for limited training data. The model uses self-generated predictions and uncertainty estimates to identify areas needing improvement, allowing it to maintain accuracy on small corpora by relying on transferred knowledge rather than requiring extensive domain-specific training data.