DreamNLP Streaming Algorithm for NLP Term Extraction
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Solution Overview
Problem
Existing natural language processing (NLP) methods are computationally intensive and memory-heavy when extracting information from large datasets like electronic medical records and financial reports, and struggle to efficiently identify frequent terms and their relationships without prior knowledge.
Innovation Solution
A modified data streaming algorithm, DreamNLP, is used to analyze streams of terms, convert them into input vectors, and generate sketch feature vectors with reduced dimensions, allowing for the identification of frequent terms and their context, while using low computational memory and Shannon entropy to evaluate distribution and visualize relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional NLP methods are used to extract information from large datasets, then information extraction capability is improved, but computational intensity and memory usage increase significantly
Solution Approach 1:
The patent extracts only the most frequent terms from large datasets using a streaming algorithm, rather than processing all terms. This selective extraction approach maintains information extraction capability while significantly reducing computational intensity and memory usage by focusing only on high-frequency terms.
Solution Approach 2:
The patent segments the data processing task into streaming processing of terms, converting them into vectors and sketch feature vectors in stages. This segmentation allows the system to handle large datasets incrementally without requiring all data to be loaded into memory simultaneously, reducing computational intensity.
2Reliability
If traditional NLP methods are used to extract information from large datasets, then information extraction capability is improved, but memory usage increases significantly
Solution Approach 1:
The patent extracts only the most frequent terms from large datasets using a streaming algorithm, rather than processing all terms. This selective extraction approach maintains information extraction capability while significantly reducing memory usage by focusing only on high-frequency terms.
Solution Approach 2:
The patent converts terms into vectors and then into sketch feature vectors with reduced dimensions. This dimensionality reduction allows the system to represent complex term relationships using fewer memory resources, maintaining information extraction capability while reducing memory usage.
3Measurement precision
If all unique terms in large corpora are stored in memory to find frequent terms, then term frequency analysis is improved, but memory requirements increase significantly
Solution Approach 1:
The patent extracts only the most frequent terms from large datasets using a streaming algorithm, rather than storing all unique terms in memory. This approach maintains accurate term frequency analysis for the most important terms while significantly reducing memory requirements.
Solution Approach 2:
The patent uses a streaming algorithm that processes terms sequentially rather than loading all terms into memory. This partial processing approach provides sufficient term frequency analysis for frequent terms without requiring memory for all unique terms, achieving acceptable precision with reduced memory usage.
Data Source
AI summary
System and methods for obtaining frequent terms in a set of data is provided. The method comprises obtaining the set of data comprising a stream of terms; analyzing the stream of terms using a modified data streaming generator to find a set of frequent terms; forming a dictionary of terms based on the set of most frequent terms; identifying one or more of the set of frequent terms in the set of data; and analyzing the one or more of the set of frequent terms in the set of data to determine a context and/or structure based on neighboring terms in the set of data; providing a visualization tool for the interactions with the frequent terms and the application at hand.