KazCausal: Manual Annotation of Causal Constructions in the Kazakh Language

Abstract

This article presents KazCausal, a manual annotation scheme developed to identify causal constructions in Kazakh language texts. The research aims to create a formalized corpus that reflects the morphological, syntactic, and semantic aspects of causality expression in the agglutinative structure of the Kazakh language. The scheme supports multiple labels and overlapping relationships, such as direct consequence, motivation, sanction, emotional conditioning, and indefinite causality. The corpus includes 681 sentences, 150 of which are marked as containing causal relationships. Inter-annotator agreement analysis demonstrates a high level of consistency. The results can be used in NLP tasks, including model training and automatic extraction of causal relationships.

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