Abusive Language Detection via Character N-gram Machine Learning
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Solution Overview
Problem
Current methods for detecting abusive language online, such as blacklists and regular expressions, are inadequate in identifying subtle forms of hate speech and are prone to false positives, as they fail to consider context and the evolving nature of abusive terms.
Innovation Solution
The use of character N-grams, which are contiguous sequences of characters, to identify features in text that are then analyzed using a machine learning model to generate a score for determining the presence of abusive language, incorporating linguistic, syntactic, and semantic features to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If blacklists and regular expressions are used to detect abusive language, then the detection method is simple and easy to implement, but the detection accuracy is low and produces many false positives
Solution Approach 1:
The patent transforms the detection approach from keyword-based (blacklists) to character N-gram based features, and from rule-based (regular expressions) to machine learning model-based detection. This parameter change in the detection methodology enables the system to capture contextual patterns and subtle forms of abusive language while maintaining automated processing capability.
Solution Approach 2:
The patent replaces the mechanical rule-based system (blacklists and regular expressions) with a machine learning model that automatically learns detection patterns from training data. This substitution allows the system to adapt to evolving abusive language patterns without manual rule updates, significantly improving detection accuracy while reducing false positives.
2Ease of repair
If blacklists and regular expressions are used to detect abusive language, then the system is simple to maintain, but it cannot identify subtle forms of hate speech and evolves slowly
Solution Approach 1:
The patent implements a dynamic detection system using machine learning models that continuously adapt to new abusive language patterns through retraining on updated datasets. Unlike static blacklists and regular expressions, the model can evolve its detection capabilities to identify subtle forms of hate speech and emerging abusive language variants, maintaining high adaptability while remaining computationally efficient.
Solution Approach 2:
The patent employs a training phase where the machine learning model learns from labeled training data containing examples of abusive and non-abusive language. This preliminary action of pre-training enables the system to develop robust detection patterns before actual deployment, allowing it to identify subtle abusive language forms without requiring continuous manual rule updates.
3Measurement precision
If context and variations of abusive terms are considered, then detection accuracy improves, but the complexity of the detection system increases
Solution Approach 1:
The patent segments text into character N-grams (contiguous sequences of characters) as fundamental units for analysis. This segmentation approach allows the system to capture contextual patterns and variations of abusive terms without requiring complex sentence-level or document-level analysis. The N-gram features provide a balanced representation that improves detection accuracy while maintaining computational efficiency.
Solution Approach 2:
The patent introduces character N-gram features as an intermediary representation between raw text and the machine learning model. These N-gram features serve as a bridge that captures contextual information and term variations in a structured format, enabling the model to learn complex patterns without directly processing the full complexity of natural language context, thus managing system complexity effectively.
Data Source
AI summary
Methods and apparatus for detecting abusive language are disclosed. In one embodiment, a set of character N-grams is ascertained for a set of text. Feature values for a plurality of features of the set of text are determined, based, at least in part, on the set of character N-grams. A computer-generated model is applied to the feature values for the plurality of features to generate a score for the set of text, where the model includes a plurality of weights, each of the weights corresponding to one of the features. It may then be determined whether the set of text includes abusive language based, at least in part, on the score.