Semantic Network Topic Deviation Detection in Document Drafting
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Solution Overview
Problem
Current natural language processing technologies lack effective guidance during document drafting, failing to accurately monitor and alert users when sections of text deviate from a target topic, leading to inconsistencies and disorganization.
Innovation Solution
A system and method that utilizes a processor to retrieve sections of text, extract local topics, generate a semantic network, and determine deviation values between local and target topics, alerting users when the deviation exceeds a threshold, thereby providing real-time guidance on maintaining topic relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing technologies are used for document analysis, then information extraction and categorization capabilities are improved, but real-time guidance during document drafting is not provided
Solution Approach 1:
The system continuously monitors document sections against the target topic and provides real-time feedback to users through alerts when deviations are detected. This feedback mechanism enables users to maintain topic relevance during the drafting process by immediately addressing off-topic sections, thus bridging the gap between accurate topic detection and ease of operation during writing.
Solution Approach 2:
The system automatically extracts local topics from document sections and compares them with the target topic without requiring manual intervention. This self-service capability allows the system to independently perform topic monitoring and guidance, improving real-time guidance availability while maintaining high topic relevance detection accuracy.
2Stability of the object's composition
If topic monitoring and alerting functionality is added to NLP systems, then document coherence is improved, but system complexity increases
Solution Approach 1:
The system divides the document into discrete sections and extracts local topics from each section independently. This segmentation allows the complex task of maintaining document coherence to be broken down into manageable units, where each section can be monitored and evaluated separately, reducing the overall system complexity while improving coherence stability.
Solution Approach 2:
The system uses deviation values as a parameter to quantify the distance between local topics and the target topic. By transforming the qualitative assessment of topic relevance into a quantitative parameter, the system simplifies the decision-making process for identifying off-topic sections, thereby reducing architectural complexity while maintaining document coherence.
3Stability of the object's composition
If real-time topic deviation detection is implemented, then document organization is improved, but processing time increases
Solution Approach 1:
The system performs topic extraction and deviation detection on document sections rather than the entire document at once. This partial action approach allows real-time monitoring and organization improvements while reducing processing time by focusing computational resources only on the current section being drafted, rather than analyzing the complete document structure.
Data Source
AI summary
A method, computer program product and computer system to provide topic guide during document drafting is provided. A processor retrieves at least one section of text from a document. A processor receives a target topic for the document. A processor extracts at least one local topic from the at least one section of text. A processor generates a semantic network comprising the at least one local topic and the target topic. A processor determines a deviation value for the at least one local topic based on a distance between the at least one local topic and the target topic in the semantic network. A processor, in response to the deviation value exceeding a threshold value, alerts a user that the at least one section of text from the document is off-topic from the target topic.