GUI Tool for Text Embedding Visualization and Labeling
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Solution Overview
Problem
Current technologies lack an intuitive graphical user interface for creating data-driven taxonomies and efficiently labeling data for natural language processing models, particularly in visualizing and understanding topic variants through text embeddings.
Innovation Solution
A Graphical User Interface (GUI) system that utilizes manifold layout algorithms and machine learning model introspection to visualize text embeddings, allowing users to upload documents, compute vector representations, and interactively label and correct data for improved model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text embeddings are computed and visualized using manifold layout algorithms, then topic variants can be discovered and understood intuitively, but the computational complexity and processing time increase
Solution Approach 1:
The system pre-computes text embeddings for all documents in the corpus before visualization. This preliminary action allows the manifold layout algorithm to work with pre-processed vector representations rather than raw text, significantly reducing the computational time required during the actual visualization and interaction phase while maintaining the ability to intuitively discover topic variants
Solution Approach 2:
The patent introduces text embeddings as an intermediary representation between raw text documents and the manifold layout visualization. This intermediary layer transforms unstructured text into structured vector representations that can be efficiently processed by dimensionality reduction algorithms, enabling intuitive topic discovery without directly computing complex relationships between raw text documents
2Productivity
If a graphical user interface is provided for interactive labeling and correction of training data, then labeling efficiency is enhanced, but the system complexity increases
Solution Approach 1:
The GUI system automatically performs several functions that would otherwise require manual intervention: it auto-generates initial labels based on text embeddings, automatically updates the training corpus when corrections are made, and re-computes embeddings for corrected documents. This self-service capability allows the system to maintain high labeling efficiency while reducing the operational complexity users must manage
Solution Approach 2:
The system implements continuous feedback loops where user corrections to labels are immediately reflected in the training data, which then triggers automatic re-training of the embedding model. This feedback mechanism allows the system to adapt to user corrections in real-time, enhancing labeling efficiency by eliminating manual re-processing steps while the automated feedback loop manages the underlying complexity
3Manufacturing precision
If the GUI allows users to correct errors in training data with automatic model re-training, then model accuracy is improved, but the computational resources and time required increase
Solution Approach 1:
When users correct errors in training data, the system performs partial re-training by only re-computing embeddings for the corrected documents and their related texts, rather than re-training the entire model from scratch. This partial action approach maintains model accuracy by incorporating corrections while significantly reducing the computational time and resources required compared to full re-training
Solution Approach 2:
The system pre-computes and caches text embeddings for all documents in advance. When corrections are made to training data, it leverages these pre-computed embeddings and only performs incremental updates rather than complete re-computation. This preliminary preparation allows the system to improve model accuracy through corrections while minimizing the additional computational time required
Data Source
AI summary
A graphical user interface comprising a data interface for receiving a set of text documents; a calculator for transforming each received text document into a vector of a n-dimensional manifold, n being larger than 2; transforming each n-dimensional manifold vector into a 2-D data; generating a first mapping of what 2-D data corresponds to what n-dimensional manifold vector; and generating a second mapping of what 2-D data corresponds to what received text document; a first user display arranged for displaying each 2-D data on a 2-D plane; a first user interface arranged for allowing a user to select a subset of the displayed 2-D data; wherein selecting said subset of 2-D data automatically selects the n-dimensional manifold vectors that correspond to said subset of 2-D data; and a second user display arranged for displaying a list of the text documents that correspond to said subset of 2-D data.