Zero-Shot Event Extraction Using Context-Definition Alignment

The patent pre-aligns event type definitions with contextual sentences during offline training, building a shared embedding space that captures semantic relationships between events and their contexts. This preliminary alignment enables the model to handle new event types during inference without requiring retraining, as the model can leverage the pre-learned semantic representations to classify events outside the original ontology.

The model achieves universality by training encoders that can represent both event mentions and event type definitions in a unified embedding space. The same encoder architecture processes both training data and new event types, allowing the system to generalize from known event patterns to unseen event types while maintaining classification accuracy.

The patent segments the event extraction process into distinct stages: offline training with context-definition alignment, query-specific warming, and inference. During inference, the model only processes event types relevant to the query context, rather than evaluating all possible event types. This segmentation reduces computational cost while maintaining coverage of relevant event types through the shared embedding space.

The model performs partial action by focusing computational resources on event types that are semantically relevant to the input context, rather than exhaustively processing all possible event types. The shared embedding space enables efficient retrieval of relevant event type representations, reducing the effective computational burden while maintaining comprehensive event type coverage for the given context.

The patent performs preliminary alignment of event type definitions with contextual sentences during offline training, pre-computing shared embeddings that capture semantic relationships. This preliminary action eliminates the need for expensive real-time alignment computations during inference, enabling fast event extraction while maintaining high accuracy through the pre-learned semantic representations.

The patent replaces complex mechanical alignment processes with a shared embedding space approach. Instead of performing computationally intensive alignment operations during inference, the model uses pre-computed embeddings that directly represent semantic relationships, substituting the mechanical alignment process with a more efficient vector-based similarity computation that enables real-time processing.