A four-module neural architecture for the automatic extraction and classification of causal relations in text.

Authors

Roman Taberkhan, Nurbolat Tasbolatuly, Madina Sambetbayeva, Saule Tazhibayeva, Nurmira Zhumay, Bayangali Abdygalym, Mira Kaldarova

Published in

Frontiers in artificial intelligence. Volume 9. Pages 1848216. Epub Jul 23, 2026.

Abstract

This article presents a four-module system for the automatic extraction and classification of causal relationships from texts in the Kazakh language, based on the fine-tuning of the KazBERT transformer language model. The proposed architecture includes four specialized modules: recognition of lexical causality markers (Token Classification, B/I-MARKER); segmentation of cause-effect clauses (Token Classification, B/I-CAUSE · B/I-EFFECT); classification of Tv forms of markers (Sequence Classification, 16 classes); determination of the type of the marker's syntactic construction-Model Group (Sequence Classification: SYNTHETIC/ANALYTIC/ANALYTICO-SYNTHETIC). The training was conducted using an original annotated corpus consisting of 3,223 sentences in the Kazakh language. The architecture is supplemented by a deterministic positional inversion algorithm for explanatory markers (sebebi, öitkenı, sondyqtan, etc.), which automatically restores the correct CAUSE-EFFECT argument order. Experiments have demonstrated that KazBERT outperforms the baseline models XLM-RoBERTa and mBERT: macro-F1 scores were 0.901 (tags), 0.865 (clauses), 0.884 (Tv-form), and 0.927 (construction type). The scientific novelty lies in the first publicly released four-level annotated corpus of Kazakh causal constructions, the operationalization of the established Turkological synthetic/analytic distinction-extended with a corpus-attested ANALYTICO-SYNTHETIC class-as a four-module annotation target, and a deterministic positional-inversion post-processor that corrects systematic argument-order errors for analytic markers.

PMID:
42564329
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.

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