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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 24·5 min read
Trilingual Topic Modeling of Sri Lankan Parliamentary Debates
TL;DR
An end-to-end framework for trilingual topic modeling of Sri Lankan parliamentary debates was developed, successfully identifying 30 macro-topics across Sinhala, Tamil, and English. This approach outperformed traditional methods, achieving a cluster purity of 0.673 and aligning with significant national events from 2017 to 2026.
✦ Why It Matters
Consider implementing similar NLP frameworks to analyze multilingual data in your projects today.
Key Takeaways
- Explore LLMs for text extraction in multilingual datasets.
- Utilize density-based clustering for effective topic modeling.
- Implement hybrid methods to improve interpretability of results.
- Analyze temporal trajectories of topics to align with significant events.
How It Works
The framework employs large language models (LLMs) to extract text from complex PDF formats, followed by multilingual embeddings that represent the text in a shared semantic space. Density-based clustering is then applied to identify topics without requiring labeled data, allowing for the discovery of thematic structures across different languages.
The BiTopic extension enhances the interpretability of the results by recovering speeches that may otherwise be considered noise.