Cross-Domain Entity Extraction Using Bidirectional LSTM
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Solution Overview
Problem
Traditional text extraction systems are domain-specific, resource-intensive, and lack flexibility in extracting text across different domains, relying heavily on large sets of supervised data and requiring significant memory and training resources.
Innovation Solution
A method and system for extracting relevant entities from a text corpus using a processor-based entity extraction system that determines features, calculates scores, and extracts entities based on predefined feature heads and templates, allowing for cross-domain extraction and reducing resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional text extraction systems are used, then text can be extracted for a specific domain, but the systems cannot extract text across different domains and require large sets of supervised data consuming significant memory and training resources
Solution Approach 1:
The patent creates a universal entity extraction system that can extract entities across multiple domains using a single trained model. The system employs a bidirectional LSTM network trained on a domain-specific corpus (e.g., biomedical texts) that can then extract entities from various domains including but not limited to the training domain. This eliminates the need for separate domain-specific systems and large supervised datasets for each domain, thereby reducing memory and training resource requirements while improving cross-domain adaptability.
Solution Approach 2:
The system performs preliminary training on a domain-specific corpus to create a pre-trained entity extraction model. This pre-trained model captures domain-specific knowledge and entity patterns that can then be applied to extract entities from texts across different domains without requiring additional domain-specific training data. The preliminary action of training on a focused corpus reduces the need for large supervised datasets when deploying across multiple domains.
2Adaptability or versatility
If custom methods are employed to extract specific text from the text corpus, then text extraction can be performed, but the process is very tedious and lacks flexibility to extract text not defined in the system
Solution Approach 1:
The entity extraction system automatically identifies and extracts relevant entities from text without requiring manual configuration or customization for each extraction task. The bidirectional LSTM model self-adapts to extract entities based on patterns learned during training, eliminating the need for tedious custom method development. Users simply need to specify the entity type of interest, and the system handles the extraction process autonomously, providing flexibility to extract different text types without increasing process complexity.
Solution Approach 2:
The system changes the approach from custom method configuration to model-based extraction by adjusting the underlying parameters and architecture. Instead of manually defining extraction rules for each text type, the system uses a trained neural network model that automatically adapts to different entity extraction tasks by processing text through learned patterns, thereby providing flexibility without increasing operational complexity.
3Measurement precision
If traditional extraction systems are used, then text extraction can be performed for a specific domain, but additional cost and resources are incurred for training with large sets of supervised data
Solution Approach 1:
The system uses a focused, domain-specific training corpus rather than attempting to train on all possible domains equally. By concentrating training resources on a specific domain (e.g., biomedical texts), the system achieves high extraction accuracy for that domain and can then leverage the learned patterns across related domains. This partial action approach—training deeply on one domain rather than superficially on many—maintains measurement precision while reducing the total quantity of supervised data required.
Data Source
AI summary
The present disclosure discloses method and system for extracting relevant entities from a text corpus. The method comprises receiving, by the entity extraction computing device, a text corpus and an entity, determining at least one feature for each block of text from the text corpus, where the at least one feature corresponds to predefined one or more feature heads, calculating a score for each block of text from the text corpus based on training of the entity extraction system, determining a template from one or more templates based on the score, where the one or more templates are generated based on the training of the entity extraction system, and extracting at least one relevant entity from the text corpus, with respect to the entity, based on the template. The method and system disclosed in the present disclosure may be used to extract relevant entities across various domains by training the system.