Hybrid Entity Recognition for Composite Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to accurately recognize both basic and composite entities in unstructured data due to lack of context, semantics, and syntax understanding, particularly struggling with similar entities of the same class, such as credit and debit amounts.
Innovation Solution
A hybrid entity recognition system that preprocesses input sentences, uses POS tagging, and converts text into numeric vectors to identify both basic and composite entities, employing rules-based and memory-based recognition modules to differentiate between similar entities based on linguistic patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior systems use basic entity recognition methods, then processing speed is maintained, but accuracy in recognizing entities and their context deteriorates
Solution Approach 1:
The system segments entity recognition into multiple hierarchical levels: first-level entities (basic entities like person names, organizations, locations) are identified separately from second-level entities (composite entities like credit amount, debit amount). This segmentation allows the system to apply different recognition strategies to different entity types, improving overall accuracy while managing complexity through modular processing stages.
Solution Approach 2:
The system transitions from traditional single-dimensional entity recognition to multi-dimensional analysis by simultaneously considering context, semantics, and syntax. This dimensional expansion enables the system to differentiate between similar entities (e.g., credit amount vs. debit amount) by analyzing multiple linguistic dimensions concurrently, thereby improving recognition accuracy.
2Adaptability or versatility
If prior systems recognize basic entities only, then processing simplicity is maintained, but ability to recognize composite entities deteriorates
Solution Approach 1:
The system implements dynamic entity recognition where the recognition process adapts based on the entity type. First-level entities are recognized using standard NER techniques, while second-level composite entities are recognized using additional linguistic pattern analysis. This dynamic approach allows the system to handle diverse entity types without requiring all recognition modules to operate simultaneously for every input, balancing versatility with manageable complexity.
3Measurement precision
If systems do not determine context and semantics simultaneously, then processing speed is maintained, but entity recognition accuracy deteriorates
Solution Approach 1:
The system performs preliminary processing by first identifying first-level entities and their basic properties before proceeding to second-level entity recognition. This preliminary action establishes a foundation of contextual understanding that accelerates subsequent semantic analysis, allowing the system to determine context and semantics in an optimized sequence rather than requiring simultaneous processing of all elements.
Data Source
AI summary
A hybrid entity recognition system and accompanying method identify composite entities based on machine learning. An input sentence is received and is preprocessed to remove extraneous information, perform spelling correction, and perform grammar correction to generate a cleaned input sentence. A POS tagger tags parts of speech of the cleaned input sentence. A rules based entity recognizer module identifies first level entities in the cleaned input sentence. The cleaned input sentence is converted and translated into numeric vectors. Basic and composite entities are extracted from the cleaned input sentence using the numeric vectors.