Deep Learning Event Extraction Using BERT and BiLSTM-CRF
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Solution Overview
Problem
Current methods for extracting events from unstructured natural language texts are inefficient and require significant manual effort, leading to challenges in accurately and quickly identifying event types, participants, and timing within large volumes of information.
Innovation Solution
A method utilizing deep learning models for trigger word and argument extraction, which analyzes input text to determine trigger words and their corresponding event types and arguments, reducing the need for artificial feature construction and improving extraction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used for event extraction, then extraction accuracy can be maintained through human judgment, but extraction efficiency and productivity are significantly reduced due to the time-consuming nature of manual analysis
Solution Approach 1:
The patent replaces manual mechanical analysis with deep learning models (BERT, BiLSTM, CRF) that automatically extract events from text. The system substitutes human cognitive processing with neural network architectures that learn patterns from training data, eliminating the need for manual feature construction and significantly improving extraction efficiency while maintaining accuracy
Solution Approach 2:
The patent transforms the event extraction problem into a parameter-based classification task by defining specific event types, trigger words, and argument roles as learnable parameters. The deep learning models optimize these parameters during training to automatically identify events, converting a subjective manual process into an objective computational optimization problem
2Ease of manufacture
If manual feature construction is used for event extraction, then extraction can be performed with simple models, but the construction cost and complexity increase significantly
Solution Approach 1:
The patent performs preliminary action by pre-training language models (BERT) on large corpora before applying them to event extraction. This pre-training phase automatically learns general language patterns and domain-specific features, eliminating the need for manual feature engineering in the downstream extraction task and simplifying system implementation
Solution Approach 2:
The deep learning models perform self-service by automatically learning relevant features and patterns from training data without human intervention. The BiLSTM and CRF components adaptively adjust their parameters during training to optimize event extraction performance, replacing manual feature construction with autonomous model learning
3Measurement precision
If deep learning models are used for trigger word and argument extraction, then event extraction accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the event extraction task into three distinct components handled by specialized models: trigger word identification (BERT-based), argument extraction (BiLSTM-CRF), and event type classification. This segmentation allows each component to be optimized independently while working together to achieve high overall accuracy, managing complexity through functional decomposition
Data Source
AI summary
A method for extracting an event from a text including obtaining an input text; inputting the input text into a model for extracting trigger words to obtain a trigger word extraction result of the input text; inputting the input text and the trigger word extraction result into a model for extracting arguments to obtain an argument extraction result of the input text; and obtaining an event extraction result of the input text according to the trigger word extraction result and the argument extraction result.