arXiv:2605.30465v1 Announce Type: new Abstract: Multi-label topic classification without labeled training data is a challenging task, specially when documents contain complex relational information. We present a zero-shot multi-label topic classification framework and systematically investigate how per-article knowledge graph augmentation affects its performance. The base framework classifies topics in documents without labeled training data and has four variants: article-only classification, keyword-enhanced classification, and self-consistency decoding variants of both. Then, we augment each
The proliferation of unlabeled data and the increasing complexity of information necessitate advanced methods for efficient topic classification without extensive human annotation.
This research provides a more efficient and robust method for categorizing large volumes of information, which is critical for knowledge management, search, and autonomous AI systems operating in complex information environments.
The ability to accurately classify topics in document streams without requiring labeled training data and with enhanced performance via knowledge graphs significantly improves the scalability and adaptability of information processing systems.
- · AI developers
- · Data scientists
- · Information retrieval systems
- · Knowledge management platforms
- · Manual data annotation services
- · Systems heavily reliant on human-labeled datasets
Improved performance and efficiency in zero-shot topic classification, particularly for complex, multi-label tasks.
Reduced operational costs and faster deployment cycles for AI applications that require content understanding and categorization.
Accelerated development of more sophisticated AI agents capable of autonomous information assimilation and nuanced decision-making through better contextual understanding.
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Read at arXiv cs.CL