Natural Language Processing
Chinese Implicit Offensive Speech Detection Based on Knowledge Graph and Fuzzy Semantic
This paper introduces the Enhanced-BERT-Mate-Ambiguity (EBMA) model, a novel fuzzy semantic interpretation framework leveraging BERT and knowledge graphs to detect implicit offensive speech on Chinese social platforms. It addresses the significant challenge posed by strategies like metaphors and abbreviations used to obscure aggressive intent. The research collected the first Chinese implicit offensive speech dataset (54,714 comments) from Weibo. Extensive experiments confirm EBMA's superior performance, achieving an accuracy of 95.83% and an F1-score of 95.52%, outperforming state-of-the-art models. The model extracts semantic, emotional, metaphorical, and ambiguity features, enhanced by a knowledge graph and attention mechanism for feature fusion.
Executive Impact
Our model, EBMA, sets a new standard for detecting implicit offensive speech in Chinese, offering unparalleled accuracy and robust performance for social media content moderation.
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Chinese Dataset
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Knowledge Enhancement Module
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Semantic Extraction Module
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Multi-feature Module (Emotion, Metaphor, Ambiguity)
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Feature Fusion Module
Comparative Model Performance (Binary Classification)
Our EBMA model consistently outperforms state-of-the-art methods in binary classification, demonstrating superior accuracy and F1-score. This highlights the effectiveness of integrating knowledge graphs and fuzzy semantic features for implicit offensive speech detection.
| Model | Accuracy | F1-score |
|---|---|---|
| EBMA (Our Model) | 95.83% | 95.52% |
| BMA (State-of-the-art) | 93.92% | 93.63% |
| BERT | 90.26% | 89.40% |
| Bi-LSTM | 86.27% | 85.53% |
Challenges in Implicit Speech Detection
While EBMA performs robustly, implicit offensive speech remains challenging. Our analysis of misclassified cases reveals common issues:
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Sarcasm Misinterpretation: A comment like 'It's better for girls not to eat chocolate. Eating chocolate will make you gain weight.' (True: Offensive, Predicted: Non-offensive) was misclassified, indicating difficulty with subtle irony. Requires more sophisticated contextual understanding beyond explicit features.
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Uncommon Internet Slang: A comment such as 'Brother, please lower the resolution on your face!' (True: Offensive, Predicted: Non-offensive) using less common slang was missed. Knowledge graph needs continuous updates to cover evolving internet lexicon.
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Lack of Context: Comments like 'You should lie down on the ground.' (True: Offensive, Predicted: Non-offensive) or 'The host has to be Liu Xiaoxiao, and it must be the live broadcast version' (True: Non-offensive, Predicted: Offensive) were problematic due to insufficient surrounding information. Highlights the need for richer contextual signals or multi-turn conversational analysis.
These cases underscore that implicit offensive speech, especially with sarcasm and evolving slang, often requires a deeper understanding of human intent and external context, which even humans can struggle with. Future work will focus on integrating more dynamic contextual information.
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Estimated Annual Savings
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Accelerate Your AI Implementation
A phased approach to integrate the EBMA model into your operations, ensuring smooth deployment and maximum impact.
Phase 1: Data Acquisition & Knowledge Graph Expansion
Gather and annotate Chinese social media data to enrich the WIOS-Dataset. Continuously expand the external knowledge graph with new slang, metaphors, and ambiguities to keep it current. (Est. 2-3 Months)
Phase 2: EBMA Model Refinement & Feature Integration
Optimize BERT-based semantic extraction and refine the multi-feature modules (emotion, metaphor, ambiguity). Enhance the attention mechanism for more robust feature fusion. (Est. 3-4 Months)
Phase 3: Robustness & Generalization Testing
Conduct extensive tests under varying conditions (noise, data imbalance, cross-domain) to ensure the model's stability and adaptability. Focus on improving performance for subtle and highly ambiguous cases. (Est. 2-3 Months)
Phase 4: API Development & Platform Integration
Develop a scalable API for the EBMA model, allowing seamless integration with existing social media monitoring and content moderation platforms. Implement real-time processing capabilities. (Est. 4-5 Months)
Phase 5: Continuous Learning & Feedback Loop
Establish a system for continuous model retraining with new data and user feedback. Monitor performance in live environments and iterate on model improvements to adapt to evolving linguistic patterns. (Ongoing)
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