Trilingual Topic Modeling of Sri Lankan Parliamentary Debates
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Computer Science > Computation and Language
arXiv:2608.20365 (cs)
[Submitted on 18 Jun 2026]
Title:Trilingual Topic Modeling of Sri Lankan Parliamentary Debates
Authors:Himath Dhanapala, Haren Daishika, Himandhi Kuruppu, Sithija Seneviratne, Ashini Kavindya, Patalee Narasinghe, Sandeepa Weerasekara, Nisansa de Silva, Sandareka Wickramanayake
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Abstract:Sri Lankan parliamentary debates (Hansards) constitute a trilingual corpus of speeches in Sinhala, Tamil, and English, including code-mixed content, yet remain inaccessible to standard NLP pipelines due to layout-complex PDFs, multilingual scripts, and agglutinative morphology. We present an end-to-end framework that addresses these challenges through LLM-based text extraction followed by a multilingual embedding and density-based clustering pipeline for topic modeling. A hybrid semantic-lexical extension, BiTopic, is further explored to improve interpretability and recover speeches otherwise discarded as noise. Applied to 19,553 speeches spanning 2017-2026, the pipeline recovers 30 macro-topics achieving a cluster purity (BCP) of 0.673, whose temporal trajectories align unsupervised with major national events including the 2019 Easter Sunday attacks and the 2022 economic crisis. Traditional LDA fails on this corpus due to cross-lingual fragmentation, whereas the proposed approach successfully identifies thematic structure across all three languages without supervision.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.20365 [cs.CL] |
| (or arXiv:2608.20365v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.20365
arXiv-issued DOI via DataCite
|
Submission history
From: Himandhi Kuruppu [view email][v1] Thu, 18 Jun 2026 19:00:11 UTC (21,734 KB)
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