Automatic Classification and Semantic Retrieval for Low-Resource Parliamentary Archives: A Case Study of the DRC Senate

Abstract

Parliamentary archives preserve the administrative, legal and political memory of legislative institutions. At the Senate of the Democratic Republic of the Congo, many documents are scanned PDF files, which makes manual indexing, classification and retrieval slow. This study does not propose a new algorithm. Its contribution is the applied integration of automatic classification and hybrid semantic retrieval in a French, OCR-based and low-resource parliamentary archive. The corpus contains 420 documents classified into five categories: orders, decrees, laws, ordinances and minutes. The models include TF-IDF with linear SVM, logistic regression and Naive Bayes, CamemBERT embeddings with logistic regression, and a weighted probabilistic fusion: P_fusion = alpha P_TFIDF + (1-alpha) P_CamemBERT, with alpha = 0.55. On the fixed test set, the hybrid model obtains accuracy = 0.786 and macro F1 = 0.815. Under stratified five-fold cross-validation, TF-IDF + linear SVM is the most stable lexical baseline, with accuracy = 0.821 +/- 0.052 and macro F1 = 0.834 +/- 0.050. These protocols are interpreted separately. For thirteen curated archival queries, hybrid retrieval performs best at k = 10, while TF-IDF obtains the highest MRR. The results show that lexical models are strong for categories with stable legal markers, whereas hybrid retrieval is useful for broader semantic access. The proposed system is a human-validated archival workflow, not a fully autonomous decision tool.