Text Classification Heatmap for Anomaly Interpretation
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Solution Overview
Problem
Text classification models struggle to provide clear insights into why specific words contribute to classification anomalies, making it difficult for users to identify and address these anomalies effectively, as the models operate as black boxes and do not transparently disclose the underlying data patterns or feature weights.
Innovation Solution
A method that decomposes the scores of extracted features into word-level scores and assigns heatmap values to each word, allowing users to visualize the contribution of each word to the predicted classification label, thereby facilitating the identification of problematic words and improving training data adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If text classification models operate as black boxes to maintain model integrity and security, then model protection is improved, but transparency of feature weights and data patterns deteriorates
Solution Approach 1:
The patent introduces an intermediary visualization layer (heatmap) that mediates between the protected black box model and the user. The heatmap displays word-level importance scores without exposing the underlying model architecture, weights, or training data patterns. This allows users to interpret model decisions while the model itself remains protected and unchanged.
2Loss of information
If detailed model information is disclosed to help users understand classification anomalies, then transparency is improved, but model security and protection deteriorates
Solution Approach 1:
The patent extracts only the necessary interpretability information (word-level importance scores) from the model's internal representations and presents it through a visualization interface. This extraction allows users to understand which words contribute most to classification decisions without exposing the model's proprietary architecture, trained weights, or training data distributions.
3Ease of operation
If users are provided with comprehensive feature weights and data patterns, then ability to address anomalies is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex model output into word-level importance scores that can be individually analyzed. Instead of presenting users with the entire complex feature space and weight matrices, the system breaks down the contribution of each word in the input text separately, making it easier for users to identify which specific words are driving classification anomalies.
Solution Approach 2:
The patent uses color intensity in heatmaps to visually encode the magnitude of word-level importance scores. Words that contribute more significantly to the classification are displayed with higher intensity colors, allowing users to quickly grasp the relative importance of different words without needing to interpret numerical values or understand the underlying complex feature interactions.
Data Source
AI summary
In response to running at least one testing phrase on a previously trained text classifier and identifying a separate predicted classification label based on a score calculated for each respective at least one testing phrase, a text classifier decomposes extracted features summed in the score into word-level scores for each word in the at least one testing phrase. The text classifier assigns a separate heatmap value to each of the word-level scores, each respective separate heatmap value reflecting a weight of each word-level score. The text classifier outputs the separate predicted classification label and each separate heatmap value reflecting the weight of each word-level score for defining a heatmap identifying the contribution of each word in the at least one testing phrase to the separate predicted classification label for facilitating client evaluation of text classification anomalies.