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.
Rekodi ya chanzo
Nukuu zimehamishwa kwa uhalisi kutoka kwa rekodi ya chanzo cha mbinu. Hakuna uthibitisho wa kiwango cha dai unaodokezwa kutoka kwao.
Madai yaliyotunzwa
Madai yamehifadhiwa katika daftari la ushahidi, kila moja ikiwa na tathmini yake.
Mwonekano huu haubuni tathmini ya dai wakati daftari haina yoyote.
Mbinu zinazohusiana
Zilizotengenezwa kutoka kwa grafu ya mbinu na kuonyeshwa kama uhusiano uliopendekezwa na mashine — hakuna dai la ushahidi linalodokezwa.