Information-theoretic conditioning in terminological alternations in specialized domains: The cases of Taiwan Mandarin legal language and English biomedical language

This study examines how information-theoretic correlates, specifically contextual surprisal, condition terminological alternations in specialized domains, where both domain-specific and general terms express similar concepts. Specifically, two competing theories exist. The Uniform Information Density (UID) theory proposes that the speaker would avoid abrupt information rate changes. This predicts the use of more specific variants when the surprisals are higher. Conversely, availability-based production suggests the use of more readily-accessible items with higher surprisals. This study examines the dynamics between these two potential mechanisms in the terminological use in specialized domains. Specifically, we argue that, in specialized language, due to the higher frequency of domain-specific terms, both accounts predict the use of specific items in higher-surprisal contexts. The cases of Taiwan Mandarin legal language and English biomedical language were, therefore, examined. Crucially, a current popular method for probability estimation is through large language models (LLMs). The linguistic distribution in specialized domains, however, may deviate from the general linguistic distribution on which the LLMs are trained. Thus, we propose a novel semantics-based method of estimating the token probability distribution in a given corpus that avoids the potentially different linguistic distribution and the issue of word segmentation. As expected, results indicated a positive correlation between a variable’s surprisal and the use of domain-specific variants in both cases. This supports UID-based production, and arguably also availability-based production, since more specific and frequent variants are preferred in high-surprisal contexts. Specifically, our semantics-based probability estimation outperformed LLM-based estimation and the baseline in both cases. This suggests the feasibility of semantics-based probability estimation in specialized domains.