Domain-Specific Text Normalization via Lexicon Inference
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Solution Overview
Problem
Current natural language processing techniques for text simplification lack domain-specific normalization, failing to effectively adapt text complexity to specific industrial, organizational, demographic, or geographic contexts.
Innovation Solution
A method and system for domain-specific normalization of text, which involves loading a corpus of text, determining its domain through inference or meta-data, and using a retrieved lexicon to simplify the text, ensuring language suitability for the identified domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general text simplification techniques are used, then text complexity is reduced, but domain-specific language accuracy deteriorates
Solution Approach 1:
The patent applies local quality by using domain-specific lexicons tailored to different fields (medical, legal, technical, etc.). Each domain has its own customized replacement words and simplification rules, allowing the system to simplify text appropriately for each specific domain while preserving domain-specific accuracy and terminology.
Solution Approach 2:
The system changes the parameter of language complexity by selecting from multiple lexicon levels (e.g., expert, intermediate, beginner) based on the target audience. This allows dynamic adjustment of text simplification degree while maintaining domain-specific accuracy through domain-appropriate vocabulary selection.
2Adaptability or versatility
If domain-specific lexicons are retrieved and applied, then domain adaptation improves, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-organizing and storing domain-specific lexicons in advance within the system. The lexicons are prepared and indexed before actual text processing occurs, allowing for rapid retrieval and application during simplification operations, thus reducing processing time while maintaining domain adaptation quality.
3Productivity
If text parsing and word indexing are performed, then text simplification capability improves, but system complexity increases
Solution Approach 1:
The patent applies universality by implementing a unified text processing framework that handles multiple domains and lexicon types through a single system architecture. The core processing engine remains generic and reusable across different domains, while domain-specific adaptations are achieved through configurable lexicons rather than separate processing systems, thus managing complexity efficiently.
Data Source
AI summary
Embodiments of the present invention provide a method, system and computer program product for the domain specific normalization of a corpus of text. In an embodiment of the invention, a method for domain specific normalization of a corpus of text is provided, including an industrial, organization, demographic or geographic domain. The method includes loading a corpus of text in memory of a computer and determining a domain for the corpus of text. The method also includes retrieving a lexicon of replacement words for the determined domain. Finally, the method includes text simplifying the corpus of text using the retrieved lexicon. In one aspect of the embodiment, the domain is determined through inference based upon words already presence in the corpus of text. In another aspect of the embodiment, the domain is determined based upon meta-data provided with the corpus of text.