Genetic Programming for Named Entity Recognition Pattern Generation
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Solution Overview
Problem
Existing methods for named entity recognition, such as using regular expressions or finite automata, require significant expert programming efforts and are inefficient for handling large-scale data streams with varied categories, necessitating a more automated and efficient approach.
Innovation Solution
A genetic algorithm is employed to generate pattern programs for named entity recognition, utilizing positive and negative example data strings to iteratively evolve offspring programs through crossover and mutation operations, with a fitness function evaluating conciseness, matching rates, and edit distance to select the most suitable pattern program.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual programming methods (regular expressions, finite automata) are used for named entity recognition, then the pattern programs can be designed with high precision, but the expert programming effort and time consumption increase significantly
Solution Approach 1:
The system enables automatic generation of pattern programs through genetic algorithms without requiring expert manual programming. The genetic algorithm autonomously evolves pattern programs from example data strings, making the system self-sufficient in program generation while maintaining high accuracy through fitness-based selection.
Solution Approach 2:
The patent transforms the program generation process from manual parameter design to automated evolutionary optimization. By changing the approach from hand-crafting patterns to algorithmic evolution with fitness functions, the system achieves both high accuracy and reduced time consumption through automated parameter search.
2Reliability
If manual programming is used to create pattern programs, then the programs can be optimized for specific tasks, but the scalability to handle large-scale data streams with varied categories becomes difficult
Solution Approach 1:
The genetic algorithm framework provides a universal solution that can handle multiple data categories and scales. The same automated evolution process works for different entity types (person names, addresses, phone numbers) without requiring separate manual programming for each category, enabling scalable deployment across diverse data streams.
Solution Approach 2:
The system automatically adapts to new data categories and scales by using the genetic algorithm to evolve patterns from example data strings. This self-service capability allows the system to handle large-scale varied data without manual reprogramming, maintaining reliability through automated optimization for each specific task.
3Productivity
If automated pattern generation methods are used, then the programming effort is reduced, but the complexity of the generation system increases
Solution Approach 1:
The patent replaces manual mechanical programming with an automated computational evolution system. The genetic algorithm substitutes human expert programming with algorithmic evolution, achieving high productivity while the system complexity is managed through standard evolutionary computation techniques and fitness-based optimization.
Data Source
AI summary
Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a pattern program using a genetic algorithm. The genetic algorithm operates on example data strings that represent the data categories to be recognized or extracted through named entity recognition. In the initialization stage, the initial pattern programs are generated based on example data strings that represent the data categories to be recognized or extracted through named entity recognition. Starting from the initial pattern programs, genetic operations are iteratively conducted to generate generations of offspring pattern programs. In each round of the genetic operation, offspring pattern programs are generated through the crossover operation and the mutation operation.