N-gram Preprocessing for NLP Input Size Reduction
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Solution Overview
Problem
Existing text summarization techniques struggle to efficiently reduce the size of documents while maintaining the original meaning, particularly for natural language processing applications with input size constraints.
Innovation Solution
The implementation of a preprocessing pipeline that employs labeled clustering and two-dimensional N-gram techniques to identify and remove non-relevant sentences, thereby reducing the document size to meet the input requirements of natural language processing models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If text summarization techniques are applied to reduce document size, then the document size is reduced to meet NLP input constraints, but the original meaning and essential information may be lost
Solution Approach 1:
The document is segmented into sentences, which are then evaluated individually using N-gram analysis. This segmentation allows selective removal of non-relevant sentences while preserving relevant ones, thus reducing document size without losing essential meaning.
Solution Approach 2:
The patent introduces a two-dimensional evaluation framework for sentence filtering: horizontal evaluation using N-gram frequency analysis and vertical evaluation using relevance scoring. This multi-dimensional approach enables precise differentiation between relevant and irrelevant content, reducing documents while preserving meaning.
2Quantity of substance
If preprocessing techniques are applied to reduce document size, then the document meets NLP model input requirements, but the processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-computing N-gram frequencies and building relevance models before actual summarization. This preprocessing of processing requirements allows the main summarization task to be more efficient, reducing overall complexity despite the added filtering steps.
Solution Approach 2:
The sentence filtering process is self-service in nature, where each sentence automatically evaluates its own relevance through N-gram analysis and scoring against the document's overall theme. This automated self-evaluation reduces the need for complex external processing and manual intervention.
3Quantity of substance
If sentences are removed to reduce document size, then the document becomes manageable for NLP processing, but the time required for processing may increase due to multiple evaluation passes
Solution Approach 1:
The system applies continuous useful action by iteratively processing sentences through N-gram analysis and relevance scoring until convergence is achieved. This continuous filtering process efficiently reduces document size while minimizing unnecessary processing time through early termination when sufficient reduction is achieved.
Solution Approach 2:
The patent replaces complex mechanical sentence-by-sentence analysis with automated N-gram frequency counting and statistical scoring. This substitution of manual analysis mechanisms with automated statistical processing significantly reduces processing time while maintaining filtering effectiveness.
Data Source
AI summary
Origin text content to be analyzed using natural language processing is received. A two-dimensional item sequence representation for at least a portion of the received origin text content is generated. Using one or more processors, one or more evaluation metrics are determined based on an analysis of the two-dimensional item sequence representation. A reduced version of the origin text content is automatically generated based on the one or more evaluation metrics to assist in satisfying a constraint of a natural language processing model. The reduced version of the origin text content is used as an input to the natural language processing model.