Context-Free and Context-Specific Linguistic Models for NLP
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Solution Overview
Problem
Current natural language processing systems are generic and do not account for the unique linguistic aspects of specific contexts, leading to inefficiencies in processing and understanding natural language input.
Innovation Solution
The use of a context-free linguistic model combined with a context-specific linguistic model to process natural language input, where the context-specific model is adapted for specific contexts through transform rules that control tokenization and normalization, allowing for improved semantic analysis and alignment of application components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generic natural language processing system is used, then the system is simple to implement, but it does not account for unique linguistic aspects of specific contexts leading to reduced accuracy
Solution Approach 1:
The linguistic model is segmented into context-free components and context-specific components. The context-free linguistic model handles general language processing, while context-specific linguistic models handle domain-specific processing requirements. This segmentation allows the system to achieve high accuracy for specific contexts without requiring complete redesign of the entire processing system.
Solution Approach 2:
The context-free linguistic model serves as a universal base that can be combined with multiple different context-specific linguistic models. This multi-functionality allows the same core processing framework to adapt to various contexts (medical, legal, technical, etc.) by simply swapping or loading appropriate context-specific models, thereby improving accuracy without proportionally increasing overall system complexity.
2Measurement precision
If context-specific linguistic models are used for all processing, then processing accuracy improves, but storage requirements increase
Solution Approach 1:
The linguistic model is segmented into context-free components and context-specific components. The context-free linguistic model handles general language processing, while context-specific linguistic models handle domain-specific processing requirements. This segmentation allows the system to achieve high accuracy for specific contexts without requiring complete redesign of the entire processing system.
Solution Approach 2:
Instead of loading all context-specific linguistic models simultaneously, the system loads only the context-specific model relevant to the current processing task. This partial action approach ensures that storage space is efficiently utilized by having context-specific models available on demand rather than permanently resident in memory.
3Measurement precision
If generic tokenization and normalization are used, then processing is faster, but the variability of input text reduces understanding accuracy
Solution Approach 1:
The system applies different tokenization and normalization rules based on the specific context. For example, medical text may require specific handling of drug names and dosages, while legal text may require specific handling of case references and statutory citations. This local quality approach ensures that each context receives the appropriate level of specialized processing, improving understanding accuracy without requiring excessive processing for all text types.
Data Source
AI summary
Aspects of the disclosure are directed to natural language processing. An input interface of a computing device receives input (e.g., speech input) and generates a digital signal corresponding to that input. Text corresponding to the digital signal is obtained, and the text is processed using each of a context-free and a context-specific linguistic model to generate linguistic processing results for that text. The text and linguistic processing results may be processed using a NLU model to generate an NLU recognition result corresponding to the input received at the input interface. The text and the linguistic processing results may also be annotated and used to train a NLU model. The linguistic processing results may relate to, e.g., the tokenization of portions of the text, the normalization of portions of the text, sequences of normalizations for portions of the text, and rankings and prioritization of the linguistic processing results.