Semantic Distance Calculation Using LDA Subject Extraction
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Solution Overview
Problem
Existing methods face challenges in effectively calculating semantic distances between subjects to determine their closeness and clustering them, especially with the rapid increase in unstructured natural language data, which hinders efficient information retrieval and classification.
Innovation Solution
A computer-implemented method using latent Dirichlet allocation (LDA) to extract subjects from documents, generate classification lists, calculate semantic distances, and create a distance matrix, while applying natural language processing techniques to normalize relevance values and exclude irrelevant subjects, thereby determining semantic proximity between subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LDA is used to extract subjects from documents, then subject classification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the large-scale subject classification problem into multiple stages: first using LDA to extract topics from documents, then calculating semantic distances between subjects based on these extracted topics. This segmentation allows the system to handle complex classification by breaking it down into manageable computational steps, reducing overall complexity while maintaining accuracy.
Solution Approach 2:
The patent introduces semantic distance calculations as an intermediary mechanism between subject extraction and final classification. By computing semantic distances between subjects based on LDA-extracted topics, the system creates a bridge that transforms raw topic data into meaningful classification relationships, improving accuracy without directly confronting the full complexity of the classification problem.
2Measurement precision
If semantic distance calculation is performed for all subject pairs, then clustering accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by calculating semantic distances selectively rather than for all possible subject pairs. The system focuses computational resources on calculating distances for subject pairs that are more likely to be related based on the LDA-extracted topics, thereby achieving adequate clustering accuracy without the exponential time cost of complete pairwise comparison.
Solution Approach 2:
The patent changes the parameter of semantic distance calculation from a brute-force approach to a topic-based approach. By utilizing the topic distributions extracted by LDA, the system transforms the distance calculation into a comparison of topic similarities, which significantly reduces computational time while preserving clustering accuracy.
3Quantity of substance
If all extracted subjects are included in classification lists, then coverage is improved, but noise and irrelevant information increase
Solution Approach 1:
The patent applies local quality by differentiating the treatment of subjects based on their relevance to each specific document. Instead of uniformly including all extracted subjects, the system evaluates each subject's local relevance to the document context and includes only those that meet relevance thresholds, thereby maintaining coverage of important subjects while filtering out noise.
Solution Approach 2:
The patent implements feedback mechanisms where the results of semantic distance calculations and relevance assessments are used to refine the inclusion criteria for subjects in classification lists. This feedback loop allows the system to iteratively improve the signal-to-noise ratio by adjusting which subjects are included based on their demonstrated relevance to the document and other subjects.
Data Source
AI summary
Embodiments provide a system and method for semantic distance calculation. The method can involve ingesting a plurality of documents; extracting a set of subjects from the plurality of documents using latent dirichlet allocation; for each document in the plurality of documents, generating a classification list comprising a ranking of the one or more subjects based on the relevance of each subject to the document; for each classification list, calculating the semantic distance between each subject present on the classification list; aggregating the plurality of classification lists; and creating a distance matrix containing the relative semantic distances between each member of the set of subjects.