Rapid Customization for Event Extraction
This addresses the need for efficient customization of event extraction systems for users handling diverse event types, though it is incremental as it builds on existing methods like neural networks and ACE corpus training.
The authors tackled the problem of customizing event extraction for new event types by developing a system that allows users to explore unannotated corpora to define triggers and automatically generate annotations and train neural models, achieving good performance for 67 novel event types with less than 10 minutes of human effort per type.
We present a system for rapidly customizing event extraction capability to find new event types and their arguments. The system allows a user to find, expand and filter event triggers for a new event type by exploring an unannotated corpus. The system will then automatically generate mention-level event annotation automatically, and train a Neural Network model for finding the corresponding event. Additionally, the system uses the ACE corpus to train an argument model for extracting Actor, Place, and Time arguments for any event types, including ones not seen in its training data. Experiments show that with less than 10 minutes of human effort per event type, the system achieves good performance for 67 novel event types. The code, documentation, and a demonstration video will be released as open source on github.com.