Context-Aware Offensive Text Classifier
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Solution Overview
Problem
Existing text classification systems struggle to accurately determine the context-dependent offensiveness of potentially offensive terms, often leading to unnecessary redaction of non-offensive content.
Innovation Solution
A classifier is trained using semi-supervised machine learning techniques to analyze both content-based and extrinsic context-based signals from text samples, allowing it to differentiate between offensive and non-offensive uses of potentially offensive terms, thereby reducing unnecessary redactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pre-defined list of offensive terms is used for redaction, then all instances of offensive terms are deleted, but non-offensive uses of potentially offensive terms are also removed unnecessarily
Solution Approach 1:
The patent changes the parameter from simple term matching to context-aware classification by analyzing multiple features including surrounding words, text type, and usage patterns. This allows the system to distinguish between offensive and non-offensive uses of the same term based on contextual parameters rather than treating all instances uniformly
Solution Approach 2:
The patent introduces a classifier as an intermediary between the offensive term detection and redaction processes. This classifier analyzes contextual signals and provides a probability score before redaction occurs, serving as a mediator that prevents premature or incorrect redaction decisions based solely on term presence
2Measurement precision
If context analysis is performed to determine offensiveness, then unnecessary redactions are reduced, but system complexity increases
Solution Approach 1:
The patent segments the context analysis into distinct signal types (textual context, extrinsic context, usage patterns) that are processed separately and then combined by the classifier. This modular segmentation of analysis tasks makes the complex system more manageable and interpretable while maintaining high precision
Solution Approach 2:
The patent creates a multi-functional classifier that handles multiple types of context signals (content-based and extrinsic) through a unified machine learning model. This universal approach consolidates what would otherwise require multiple separate analysis systems into a single classifier that processes diverse contextual information types
3Reliability
If manual labeling of training samples is performed, then initial classifier accuracy is achieved, but time and resources are consumed
Solution Approach 1:
The patent performs preliminary manual labeling of a small seed set of training samples to initialize the classifier. This preliminary action creates a foundation that can then be expanded through automated labeling in subsequent iterations, reducing the overall time investment compared to manually labeling all training data
Solution Approach 2:
The patent implements an iterative feedback loop where the classifier is initially trained on manually labeled samples, then used to automatically label additional samples, which are fed back into the training process to retrain and improve the classifier. This feedback mechanism progressively reduces manual labeling requirements while maintaining or improving accuracy
Data Source
AI summary
A computer-implemented method can include identifying a first set of text samples that include a particular potentially offensive term. Labels can be obtained for the first set of text samples that indicate whether the particular potentially offensive term is used in an offensive manner. A classifier can be trained based at least on the first set of text samples and the labels, the classifier being configured to use one or more signals associated with a text sample to generate a label that indicates whether a potentially offensive term in the text sample is used in an offensive manner in the text sample. The method can further include providing, to the classifier, a first text sample that includes the particular potentially offensive term, and in response, obtaining, from the classifier, a label that indicates whether the particular potentially offensive term is used in an offensive manner in the first text sample.