Semantic communication is an emerging paradigm that prioritizes transmitting task-relevant information over accurately delivering raw data bits. In this paper, we address an unequal error protection (UEP) problem in digital semantic communication, where bits of higher semantic importance require stronger protection. To quantify bit-level importance, we leverage bit-flip probabilities of semantic bits as target error protection levels, which are jointly learned with semantic encoder and decoder. We propose two novel channel coding frameworks aimed at minimizing the total blocklength while satisfying UEP constraints. First, we develop a bit-level UEP framework based on repetition coding, in which the repetition number for each bit is optimized to precisely meet its target bit-flip probability. Second, we introduce a block-level UEP framework utilizing modern channel codes, where semantic bits with similar target bit-flip probabilities are grouped to exploit coding gains. Within this framework, we propose a bit-grouping algorithm guided by finite blocklength capacity analysis. Simulation results conducted on image transmission tasks confirm that the proposed frameworks significantly outperform conventional approaches, yielding substantial improvements in both task performance and transmission efficiency.