Dynamic NLP Model Selection for Text Document Navigation
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Solution Overview
Problem
Existing technologies for navigating and representing text documents are inefficient, often requiring significant computational resources and failing to provide intuitive navigation and detailed summaries linked to specific sections of the document.
Innovation Solution
The use of multiple NLP models to generate mark-ups of text documents, where a first NLP model provides a high-level representation and a second NLP model generates a more detailed mark-up based on user interactions, allowing for efficient navigation and comprehension of document content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a single NLP model is used to process the entire text document in a generalized way, then comprehensive document processing is achieved, but significant computational processing resources are required
Solution Approach 1:
The patent segments the text document into multiple portions and applies different NLP models selectively to different portions based on user interactions. Instead of processing the entire document uniformly, the system divides the document and applies computational resources only where needed, reducing overall resource consumption while maintaining processing quality.
Solution Approach 2:
The system performs partial processing by applying detailed NLP model analysis only to portions of the document that users interact with, rather than processing the entire document with the same level of detail. This partial action approach reduces computational resources while maintaining comprehensiveness for relevant sections.
2Adaptability or versatility
If multiple NLP models are used to generate detailed mark-up for specific portions, then user-specific tailored processing is achieved, but system complexity increases
Solution Approach 1:
The system dynamically selects and applies different NLP models based on real-time user interactions with the document. The model selection and application process is flexible and adaptive, allowing the system to adjust its processing approach according to user needs without requiring a fixed complex architecture for all possible scenarios.
Solution Approach 2:
The patent extracts and applies specific NLP models only to the portions of the document that users interact with, rather than maintaining and applying all possible models to the entire document. This extraction approach reduces system complexity by only activating necessary models for specific tasks.
3Loss of information
If comprehensive mark-up is generated for the entire document, then complete document representation is achieved, but navigation efficiency decreases
Solution Approach 1:
The system applies different levels of mark-up quality to different portions of the document based on user interactions. High-detail mark-up is generated only for portions users interact with, while other portions receive minimal or no mark-up. This local quality approach maintains complete document representation where needed while reducing navigation overhead.
Solution Approach 2:
Instead of generating comprehensive mark-up for the entire document, the system generates detailed mark-up only for specific portions that users interact with. This partial action reduces the time required to generate and navigate through mark-up while maintaining complete representation of relevant sections.
Data Source
AI summary
A method includes obtaining a text document, generating a first mark-up of the text document by a first natural language processing (NLP) model, and displaying the text document and the first mark-up. The method also includes receiving a user interaction with a portion of the first mark-up. The portion of the first mark-up of the text document may be associated with a portion of the text document. The method additionally includes, based on the user interaction with the portion of the first mark-up, selecting a second NLP model by which to process the portion of the text document, where the second NLP model is different from the first NLP model. The method further includes, based on selecting the second NLP model, generating a second mark-up of the text document by processing at least the portion thereof by the second NLP model, and displaying the text document and the second mark-up.