NLP Keyword Analysis Using Forecasted Feature Matrices
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Solution Overview
Problem
Natural language processing systems face challenges in handling cultural specific semantics, such as slang terms, which introduce additional complexity due to multiple definitions of words, requiring improved methods to utilize contextual information for accurate searches.
Innovation Solution
A method involving the creation of document and word feature matrices, with forecasting and coefficient calculation to determine word ranks, enabling the generation of queries that account for multiple definitions of keywords through parallel searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NLP methods are used to process words with multiple definitions, then processing speed is maintained, but search accuracy deteriorates due to inability to handle cultural specific semantics and slang terms
Solution Approach 1:
The patent segments the processing of words with multiple definitions by creating separate feature matrices for different contextual meanings. Each definition or sense of a word is processed independently through parallel searches, allowing the system to handle cultural specific semantics and slang terms accurately without overwhelming the processing system with a single monolithic approach.
Solution Approach 2:
The patent introduces a new dimension of processing by creating feature matrices that capture contextual information across multiple dimensions (document features, word features, and their interactions). This dimensional expansion allows the system to represent and process multiple definitions of words simultaneously, improving search accuracy for ambiguous terms while maintaining manageable complexity through structured matrix operations.
2Measurement precision
If parallel searches are implemented to handle multiple word definitions, then search accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing feature matrices (document feature matrix H and word feature matrix W) before actual search queries are executed. These pre-computed matrices capture contextual relationships and multiple definitions in advance, so that during search operations, the system can quickly retrieve and combine pre-prepared information rather than computing everything from scratch, significantly reducing processing time while maintaining high accuracy.
Solution Approach 2:
The patent uses copying by creating forecasted versions of feature matrices (H' and W') that represent future or alternative states of the data. These forecasted matrices allow the system to efficiently explore multiple possible interpretations of ambiguous words without repeatedly processing the original data, reducing computational overhead and processing time while maintaining search accuracy across multiple definitions.
3Reliability
If contextual information is extensively analyzed to resolve word ambiguities, then search relevance improves, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming contextual information into structured numerical parameters within feature matrices. Instead of qualitatively analyzing contextual relationships, the system quantifies them as numerical features (document features in matrix H, word features in matrix W) that can be efficiently processed through mathematical operations. This parameter transformation maintains high search relevance while reducing computational complexity by converting complex contextual analysis into manageable numerical computations.
Solution Approach 2:
The patent introduces feature matrices as intermediary structures between raw contextual information and final search results. These matrices (H and W) serve as mediators that organize and structure contextual data in a way that simplifies subsequent processing. The matrices capture complex relationships in a standardized format, allowing the system to handle word ambiguities and cultural semantics with reduced computational complexity while maintaining high search relevance.
Data Source
AI summary
As disclosed herein, a method for generating a natural language processing query includes receiving one or more documents, wherein each document comprises a set of words, processing the one or more documents and the sets of words to provide a document content matrix V, a word feature matrix W, and a document feature matrix H, forecasting values for each entry of the word feature matrix and the document feature matrix over a selected time interval and a selected set of domains to provide a forecasted word feature matrix W′ and a forecasted document feature matrix H′, calculating a set of coefficients for forecasted document feature matrix H′ such that V=W′ *H′, determining a rank for each word of the sets of words according to the calculated set of coefficients, and generating one or more queries according to the determined ranks for each word of the set of words.