Sign Language Corpus Analysis

Sign language corpus analysis is the methodology for building and studying machine-readable, multimedia collections of signed languages, the natural visual-spatial languages of deaf communities. Because signed languages have no widely used written form, a corpus cannot be a body of text; it must be a structured collection of video recordings of deaf signers, layered with time-aligned annotation that makes the language searchable and analyzable. Trevor Johnston's 2010 account of moving from archive to corpus set out the central methodological principles, most notably ID-glossing, in which every instance of a sign is annotated with a single stable identifier linked to a lexical database so that all tokens of the same sign can be found and counted. The pipeline records signing on video, segments and ID-glosses it, links those glosses to a lexicon, aligns multiple annotation tiers to the video timeline, and then supports quantitative, corpus-based analysis of frequency, variation, and grammar. The result turns a fragile collection of recordings into reusable empirical evidence about how a signed language is actually used.