This talk presents an ongoing corpus-based study that approaches omissibility not simply as a diagnostic criterion but as an empirical phenomenon in its own right. Omissibility is a context-dependent property of a linguistic unit, established when its deletion does not affect grammatical well-formedness or the propositional content of the sentence in which it occurs. It is operationalized through a controlled deletion procedure implemented in an automated analysis pipeline applied to datasets drawn from a 160M-token Reddit corpus annotated with UDPipe 2, in which candidate units are evaluated for omissibility by GPT-OSS-120B under explicitly defined criteria. At the current stage of the project, the study examines which linguistic units are classified as omissible by the pipeline and analyses properties such as their syntactic behaviour, immediate co-text, and degree of structural embedding, thereby generating distributional profiles that support systematic comparison across linguistic units, including multifunctional items.
