Document Recommendation via Hypernym Expansion and Parallel Segmentation
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Solution Overview
Problem
Existing methods for finding content similarity in large document sets are computationally expensive and prone to mislabeling, especially when dealing with shifting domains or very large datasets, as they require retraining and serial processing, limiting their effectiveness.
Innovation Solution
A hybrid approach combining linguistic document recommendation with term frequency methods, using hypernym expansion to add additional terms to seed documents, allowing for more accurate document recommendations by broadening the language used for comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linguistic methods are used to analyze document sets and perform comparisons based on linguistic analysis, then document similarity determination accuracy is improved, but computational cost increases and performance decreases
Solution Approach 1:
The patent segments the document set into smaller subsets that can be processed in parallel. By dividing the large document collection into manageable chunks, the system maintains linguistic analysis accuracy while enabling concurrent processing across multiple computational units, thereby improving overall processing speed without sacrificing similarity determination precision.
Solution Approach 2:
The patent performs preliminary linguistic analysis and creates representative vectors for documents before the actual similarity comparison phase. By pre-processing documents to extract linguistic features and generate compact representations in advance, the system reduces the computational burden during runtime comparisons, maintaining accuracy while improving processing efficiency.
2Productivity
If term frequency methods are used to represent documents as mathematical vectors, then processing speed is improved, but document mislabeling increases
Solution Approach 1:
The patent introduces linguistic analysis as an intermediary layer between raw term frequency data and final document classification. By first applying linguistic rules and semantic understanding to interpret term frequencies, the system generates more reliable document representations that maintain processing speed while reducing mislabeling errors through enhanced semantic context.
Solution Approach 2:
The patent creates composite document representations by combining term frequency vectors with linguistic analysis results. This hybrid approach integrates the speed advantages of numerical vector operations with the accuracy benefits of linguistic understanding, producing document representations that are both computationally efficient and semantically reliable for classification tasks.
3Device complexity
If TF/IDF method is used to calculate term weights, then document vector creation is simplified, but process serialization is required which limits performance
Solution Approach 1:
The patent divides the document set into parallel processing segments, allowing TF/IDF calculations to be performed concurrently on different subsets of documents. By segmenting the workload and distributing it across multiple processing units, the system eliminates the serialization bottleneck while maintaining the simplicity of the TF/IDF weighting approach.
Solution Approach 2:
The patent calculates TF/IDF weights for only the most relevant terms in each document rather than all unique terms across the entire corpus. By focusing computational effort on a subset of high-impact terms, the system reduces the overall calculation burden and enables faster parallel processing while still achieving effective document representation.
4Measurement precision
If linguistic methods are used for document comparison, then document similarity accuracy is improved, but retraining is required when domain shifts which increases complexity
Solution Approach 1:
The patent develops linguistic analysis rules and vector representation methods that are domain-agnostic and can be applied universally across different document domains. By creating a flexible framework based on fundamental linguistic principles rather than domain-specific training, the system maintains high similarity determination accuracy across diverse domains without requiring retraining, thereby reducing adaptability complexity.
Data Source
AI summary
A system and method for document recommendation combines linguistic and term frequency approaches in order to improve overall performance and recommendation. Hypernyms are added to the source document set so that related documents can be recommended from the source document set. There are a variety of different parameters to configure that change operation of the document recommendation system.