Detecting grammaticalization through computational tools: the auxiliarization of habeo

This study investigates the grammaticalization of the Romance temporal construction ‘to have plus Perfect Participle’, employing a multifaceted approach that integrates theoretical linguistics, corpus analysis and computational methods. It critically assesses the potential of linguistic annotation pipelines and Neural Language Models to contribute to diachronic linguistic research, specifically in tracking the evolution of habeo into an auxiliary verb. The research explores whether automatic annotation models can accurately capture the early stages of grammaticalization, identifies the syntactic conditions that facilitate auxiliary annotation, and evaluates whether pre-trained models can reliably distinguish between the auxiliary and lexical uses of habeo. A corpus of Latin sentences, spanning the 3rd-5th and 8th-10th centuries, was compiled and processed using UD Pipe, revealing challenges in parsing related to data scarcity in the training treebank. The study also employs Latin BERT, a Neural Language Model, to examine the model's ability to differentiate between lexical and proto-grammatical uses of habeo. Preliminary results demonstrate that the Perfect Participle’s behavior became increasingly verbal over time, which is in line with the grammatical use of habeo. Furthermore, the study identifies a shift in the syntactic context of auxiliaries, with later periods showing a greater propensity for auxiliary use, which aligns with the observed model predictions. This work highlights the value of computational linguistic tools in diachronic studies, while also advancing our understanding of the syntactic features that characterize grammaticalization.