Semantic Clustering Lookup Table for Text Corpus Analysis
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Solution Overview
Problem
Conventional statistical and machine learning algorithms are inefficient when dealing with datasets containing a large number of unique categories, as they struggle to transform such data into a bounded, smaller number of categories suitable for analysis.
Innovation Solution
A method involving natural language processing (NLP) algorithms to identify target terms and compute weights for their relationships within a text corpus, storing these weights in a lookup table for improved runtime analysis by clustering and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional statistical and machine learning algorithms are applied to datasets with large numbers of unique categories, then the analysis can be performed, but the processing efficiency is poor and the algorithms cannot rapidly transform the data into a bounded number of categories
Solution Approach 1:
The patent applies preliminary action by pre-computing semantic relationships between terms and storing them in a lookup table before the actual machine learning analysis. The NLP algorithm analyzes the text corpus in advance to determine weights for term pairs, creating a pre-processed semantic structure that accelerates subsequent clustering operations and category transformation.
Solution Approach 2:
The patent introduces an intermediary NLP-based semantic analysis layer between the raw data and the machine learning algorithms. This intermediary component computes term relationship weights and stores them in a lookup table, serving as a mediator that transforms unstructured text data into a structured format with predefined semantic relationships, thereby improving the efficiency of downstream algorithms.
2Speed
If the number of categories in the dataset is reduced to a bounded number for efficient algorithm execution, then the processing speed improves, but the transformation process becomes computationally intensive and time-consuming
Solution Approach 1:
The system performs preliminary semantic analysis to pre-compute and store term relationship weights in a lookup table, so that during actual execution, the algorithm can rapidly access pre-calculated semantic relationships rather than computing them in real-time, thus reducing transformation time while maintaining execution speed.
Solution Approach 2:
The patent changes the parameter representation by transforming raw text data into a weighted term pair structure based on semantic relationships. Instead of working with raw categorical data, the system uses pre-computed weight parameters that represent semantic similarity, enabling faster comparison and clustering operations.
3Productivity
If a lookup table with precomputed term relationship weights is created, then runtime analysis performance improves, but the initial processing time and computational resources required to build the table increase
Solution Approach 1:
The patent accepts the time cost of preliminary table construction as a trade-off for significant runtime performance improvements. The NLP algorithm performs comprehensive semantic analysis upfront to build the lookup table with all term pair weights, enabling rapid querying and comparison during actual data analysis without repeated computational overhead.
Solution Approach 2:
The system discards the computationally expensive real-time semantic computation during runtime analysis and recovers by using the pre-stored lookup table. The initial investment in building the table is discarded in favor of efficient runtime access, where the system recovers performance by querying pre-computed weights instead of recalculating them.
Data Source
AI summary
Performing an operation comprising analyzing an electronic text corpus by a natural language processing (NLP) algorithm executing on a processor to determine a set of target terms specified in the electronic text corpus, analyzing the electronic text corpus by the NLP algorithm to determine a weight for each pair of terms in the set of target terms, and storing an indication of each pair of terms and the respective weight in a lookup table in a computer memory for runtime analysis of an input text data.