Powerful variables for knowledge representation and bracketing prediction

Abstract The acquisition of knowledge is essential for specialized translation, and the representation of specialized phraseology in terminological knowledge bases facilitates this process. The aim of this study is two-fold. Firstly, it describes how the semantic annotation of the predicate-argument structure of sentences mentioning named rivers can be addressed from the perspective of Frame-based Terminology. The results show that this approach, including the semantic variables of verb lexical domain, semantic role, and semantic category, provides valuable insights into the knowledge structures underlying the usage of named rivers in specialized texts. Secondly, this study explores whether the bracketing of a three-component multiword term can be predicted from the semantic information encoded in the sentence where the ternary compound and a named river are used as arguments. The semantic variables of lexical domain, semantic role, and semantic category allowed us to construct two machine-learning models capable of accurately predicting ternary-compound bracketing.