Professional Language Parsing System for Context-Aware Document Editing
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Solution Overview
Problem
Current natural language processing technologies struggle to understand the context of professional language, leading to misinterpretation and poor communication between professionals from different fields.
Innovation Solution
A method and system that utilize professional and non-professional parsing algorithms, combined with machine learning models, to analyze and coordinate professional language in written documents. This involves assigning scores to ingested writings, comparing them to stored professional data, and transmitting suggested modifications to align the language with the intended professional field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current natural language processing technologies are used to understand professional language, then grammatical construction can be translated, but context and professional nuances are lost leading to misinterpretation
Solution Approach 1:
The system segments the language analysis into multiple components: professional parsing algorithms analyze professional terminology and context, non-professional parsing algorithms analyze general language structure, and machine learning models integrate both to produce accurate translations that preserve professional nuances
Solution Approach 2:
The machine learning model acts as an intermediary that receives inputs from both professional and non-professional parsing algorithms, processes them together, and generates translations that accurately capture professional context while maintaining grammatical correctness
2Measurement precision
If professional parsing algorithms are used to analyze writing, then professional language accuracy improves, but system complexity increases
Solution Approach 1:
The system merges professional parsing algorithms with non-professional parsing algorithms and machine learning models into a unified processing pipeline, where the combined output produces more accurate professional language detection while the integration manages complexity through coordinated operation
Solution Approach 2:
The machine learning model serves multiple functions: it processes inputs from professional parsing algorithms, integrates them with non-professional parsing results, and generates final translations, thereby reducing the need for separate specialized systems
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for coordinating professional language is provided. The present invention may include running, by a processor, at least one professional parsing algorithm and at least one non-professional parsing algorithm on an ingested writing; assigning, by at least one of the professional parsing algorithms and at least one of the non-professional parsing algorithms, one or more initial scores to the ingested writing; determining, by a machine learning model, one or more differences between the ingested writing and stored professional data by comparing the one or more initial scores of the ingested writing to the stored professional data; determining a variation percentage score of the ingested writing based on the differences; and transmitting at least one suggested modification of the ingested writing based on the variation percentage score to a transmitting device.