Explainable Corpora Embeddings via Graph Rank Vectors
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Solution Overview
Problem
Computing systems face challenges in generating explainable embeddings from text corpora, making it difficult to rank terms and identify trends effectively.
Innovation Solution
A method is developed that constructs a graph with nodes representing terms and edges with pointwise values between pairs of terms, generating a rank vector from the graph, which is used to select terms by comparing it to a previous rank vector, allowing for the extraction of explainable corpora embeddings and trend identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated embedding generation is used, then processing efficiency is improved, but explainability deteriorates
Solution Approach 1:
The patent segments the embedding generation process into distinct components: a graph construction module that processes text corpora, a rank vector generation module that computes embeddings, and a selection module that identifies terms. This segmentation allows the system to maintain automated processing while providing interpretability through the graph structure and rank vectors, resolving the contradiction between efficiency and explainability.
Solution Approach 2:
The patent introduces intermediate representations (graph structures and rank vectors) that mediate between the raw text data and the final embeddings. These intermediaries serve as explainable artifacts that users can inspect to understand how embeddings are derived, thereby maintaining explainability while preserving automated processing capabilities.
2Measurement precision
If graph construction with pointwise values is used, then term ranking accuracy is improved, but system complexity increases
Solution Approach 1:
The patent transforms the text corpus into a graph structure where terms are nodes and pointwise mutual information values are edge weights. This parameter transformation simplifies the complex task of accurate term ranking into a more manageable graph processing problem, improving ranking accuracy while the systematic approach to graph construction keeps complexity manageable through standard algorithms.
3Measurement precision
If rank vector comparison is used, then trend identification accuracy is improved, but computational time increases
Solution Approach 1:
The patent pre-computes rank vectors for text sequences and stores them for later comparison. By performing the computationally intensive rank vector generation in advance during text processing, the system can quickly compare trends across different text sequences without repeating the expensive computation, thereby improving trend identification accuracy while minimizing real-time computational time.
Data Source
AI summary
A method extracts explainable corpora embeddings. The method includes constructing a graph with nodes representing terms from a text sequence and edges that include pointwise values generated between pairs of terms. The method further includes generating a rank vector from the graph. Elements of the rank vector correspond to the edges of the graph. A rank value, of the rank values, corresponds to a term from the text sequence. The method further includes selecting the term by comparing the rank vector to a previous rank vector generated for a previous text sequence.