Multi-Concept Latent Semantic Analysis Query Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Standard Latent Semantic Analysis (LSA) processes often overlook key concepts by focusing on only one concept of the text, failing to preserve multiple concepts during querying.
Innovation Solution
The method involves identifying distinct concepts in the query, creating separate pseudo-documents for each concept, probing the LSA term space, and combining the results to preserve multiple concepts, using techniques like agglomerative clustering and log-entropy weighting to ensure meaningful terms are clustered appropriately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard LSA processes are used to analyze text, then the processing is simple and fast, but only one concept is focused on and key concepts are overlooked
Solution Approach 1:
The patent segments the text into multiple concept clusters by first identifying terms, creating term vectors, and then clustering them into distinct concept groups. Each cluster represents a separate concept, allowing the system to process and retrieve multiple concepts simultaneously while maintaining processing efficiency through automated clustering algorithms.
2Adaptability or versatility
If multiple concepts are preserved through clustering and pseudo-documents, then query coverage breadth is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing the text to identify terms, create term vectors, and cluster them into concepts before the actual querying process. This preliminary organization of data into structured concept clusters enables the system to handle multiple concepts during querying without significantly increasing real-time processing complexity.
3Loss of information
If term vectors are clustered into multiple clusters, then multiple concepts are captured, but the computational steps increase
Solution Approach 1:
The patent changes parameters by transforming raw text into term vectors with specific mathematical properties, then using clustering algorithms that operate on these vector parameters. By representing concepts as mathematical objects with defined properties, the system can efficiently cluster and retrieve concepts using computational operations rather than complex analytical processes.
Data Source
AI summary
A method includes accessing text, identifying a plurality of terms from the text, determining a plurality of term vectors associated with the identified plurality of terms, and clustering the determined plurality of term vectors into a plurality of clusters, the plurality of clusters comprising a first and a second cluster, the first and second clusters each comprising two or more of the determined term vectors. The method further includes creating a first pseudo-document according to the first cluster, creating a second pseudo-document according to the second cluster, identifying a first set of terms associated with the first cluster using latent semantic analysis (LSA) of the first pseudo-document, identifying a second set of terms associated with the second cluster using LSA of the second pseudo-document, and combining the first and second sets of terms into a list of output terms.