A CONTEXT-AWARE NOISY-CHANNEL MODEL FOR AUTOMATIC SPELLING CORRECTION OF UZBEK USER-GENERATED TEXTS
User-generated Uzbek texts — social media comments, customer reviews and citizens’ appeals to e-government portals — contain a high proportion of spelling errors that degrade the quality of downstream social-media monitoring tasks. The paper formulates spelling correction for Uzbek as a noisy-channel decoding problem i...