Language-Independent NLP Pattern Learning for Multi-Lingual Processing
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Solution Overview
Problem
Conventional Natural Language Processing (NLP) systems lack a universal, language-independent platform for handling multiple NLP tasks efficiently and accurately, requiring language-specific resources and failing to implement iterative learning mechanisms effectively across languages.
Innovation Solution
A method and system for language-independent iterative learning in NLP that identifies NLP features, linkage relationships, and creates patterns within sentences, computes confidence scores, and assigns categories based on a trained dataset, enabling the execution of NLP tasks across languages using a semi-supervised approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional NLP systems use language-specific resources and separate handling for each language, then they can process individual languages, but they lack efficiency and accuracy when handling multiple languages and require building components from scratch for each language
Solution Approach 1:
The patent creates a universal NLP platform that handles multiple languages through a single unified architecture. The system uses language-independent features (word boundaries, character n-grams, POS tags) that work across all languages, eliminating the need for separate language-specific components. This universal approach enables the system to process multiple languages efficiently while maintaining accuracy, directly resolving the contradiction between multi-lingual versatility and processing efficiency
2Measurement precision
If conventional systems build NLP components from scratch for each language, then they can achieve language-specific accuracy, but the process is time-consuming and tedious
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing language-independent features (NLP features, linkage relationships, patterns) in a trained dataset before actual NLP task execution. This pre-processing creates a reusable knowledge base that can be quickly applied to any language without requiring time-consuming component building from scratch, while maintaining accuracy through the use of linguistically motivated features
Solution Approach 2:
The system creates simplified copies of language structures through abstract patterns that capture essential linguistic relationships without requiring full language-specific resources. By copying and adapting patterns from the trained dataset rather than building complete language models from scratch, the system achieves both accuracy and efficiency across multiple languages
3Ease of operation
If conventional systems handle multi-lingual tasks separately, then they can process each language independently, but they lack accuracy and efficiency compared to a unified approach
Solution Approach 1:
The patent merges separate language processing operations into a unified NLP platform that handles multiple languages simultaneously through shared computational resources and patterns. By combining language-independent feature extraction with language-specific data in the trained dataset, the system achieves both ease of operation (single platform for all languages) and high accuracy (through comprehensive multi-lingual training data)
Data Source
AI summary
A method and system of language independent iterative learning mechanism for Natural Language Processing (NLP) tasks is disclosed. The method includes identifying at least one NLP feature associated with a set of words within a sentence for an NLP task. The method includes creating a pattern associated with the sentence for the NLP task, based on the at least one NLP feature associated with the set of words and the linkage relationship between each subset of two adjacent words. The method further includes computing a confidence score corresponding to the pattern, based on a comparison within a trained dataset. The method further includes assigning a pattern category to the pattern, based on the confidence score and a predefined threshold score. The method further includes executing the NLP task based on the assigned pattern category.