Semantic Distance Measurement for Text Classification
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Solution Overview
Problem
Current text classification methods lack efficiency in measuring semantic distances between keywords, leading to suboptimal classification results and requiring extensive data processing, especially in handling large volumes of multilingual content and dynamic concepts.
Innovation Solution
The system employs a semantic distance measurement method that maps keywords to categories, uses a weighted vector approach to classify content, and leverages a channel builder tool to group low-level categories into higher-level channels, enabling efficient classification and handling of vast datasets without the need for extensive data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional text classification methods are used to measure semantic distances between keywords, then classification can be performed, but the processing efficiency is low and computational complexity is high when handling large volumes of content
Solution Approach 1:
The patent segments the large-scale semantic distance measurement problem into smaller sub-problems by dividing keywords into multiple batches and processing them in parallel. The classification system is divided into modular components including keyword extraction module, semantic distance calculation module, and category assignment module, which can operate independently and be scaled separately.
Solution Approach 2:
The patent implements incremental classification where only newly added or updated content is processed rather than re-classifying the entire dataset. The system performs partial updates to the classification model and semantic distance matrices, reducing computational load by avoiding redundant calculations on already-classified content.
2Measurement precision
If extensive data processing is performed to achieve accurate classification, then classification accuracy improves, but processing time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary processing by pre-computing semantic distance matrices for frequently occurring keyword pairs and storing them in cache. Common n-grams and phrases are pre-identified and pre-classified. The system builds a preliminary vocabulary and category mapping that can be quickly applied to new content without performing full semantic analysis from scratch.
Solution Approach 2:
The patent uses approximation techniques where instead of computing exact semantic distances for all keyword pairs, it uses sampled subsets and statistical estimates. The system creates simplified representations of semantic relationships that capture the essential meaning while requiring fraction of the computational resources of exact methods.
Data Source
AI summary
A system and method for performing classification using semantic distance measurements. Items of electronic content accessed by individuals over a global communications network are identified. A set of content that includes the plurality of identified items of electronic content are stored. The set of content is normalized. Each of the keywords contained the set of content is identified and a semantic distance between each of the identified keywords is measured.