Term-Association Vector Space Generation for Document Analysis
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Solution Overview
Problem
Current natural language processing technologies face challenges in quickly analyzing large sets of documents to produce useful mathematical representations, leading to inefficiencies in extracting meaningful information and relationships between terms.
Innovation Solution
A method and system that rapidly generate a term-association vector space by extracting terms and their connections from a set of documents, using a matrix representation where terms represent both rows and columns, and applying processes like truncated Singular Value Decomposition and spectral association to enhance the quality and dimensionality of the vector space, while also incorporating phrases and inverse document frequency scaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional natural language processing methods are used to analyze large document sets, then comprehensive term analysis can be achieved, but the processing time and computational resources required are excessive
Solution Approach 1:
The patent segments the large document set into smaller manageable chunks or batches that can be processed in parallel. The term extraction and vector space generation is divided into independent stages that can be executed concurrently, significantly reducing overall processing time while maintaining comprehensive analysis coverage.
Solution Approach 2:
The patent performs preliminary term extraction and frequency counting on document subsets before generating the complete vector space. By pre-processing documents in batches and caching intermediate results, the system avoids reprocessing the entire document set multiple times, thereby improving productivity while minimizing time loss.
2Measurement precision
If detailed term connection analysis is performed on all documents, then high precision term relationships are obtained, but the computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent applies local quality by focusing computational resources on identifying and analyzing only the most significant term connections within local document contexts. Rather than computing all possible term relationships across the entire corpus, the system identifies locally relevant term pairs and prioritizes their analysis, maintaining high precision while reducing overall computational complexity.
Solution Approach 2:
The patent changes parameters such as connection threshold, window size, and term frequency cutoffs to optimize the balance between precision and complexity. By dynamically adjusting these parameters based on document characteristics and analysis goals, the system achieves high measurement precision for critical term relationships while keeping device complexity manageable through selective parameter tuning.
Data Source
AI summary
Disclosed herein is a method and system for producing a term association vector space on demand for a client given a document set in electronic form. The method extracts terms from the document set, stripping out words that do not convey meaning and adding important phrases within the context of the document set to the terms. Associations between terms are calculated, subjected to further analytical processes, and collected in a matrix, whose rows are vectors defining the vector space. Additional associational data can be added by matrix arithmetic, and documents can be rendered as further vectors in the space.