Sememe Based Semantic Communications

Semantic communication, a paradigm concentrating on correctly transmitting underlying semantic information instead of bit sequences, has proved effective. Deep learning (DL) enabled methods are mainly used with basic natural language processing (NLP) techniques for the semantic communication of texts. However, most of the previous work approaches the problem by treating text as a generic continuum of sequences of textual information without leveraging underlying intrinsic and advanced linguistic properties of natural languages. A prime example of such linguistic features is the
sememes
, the smallest and indivisible semantic units of textual information (analogous to the Periodic Table of Matter). This paper proposes Sememe-based Semantic Communications (SememeSC), a semantic communication paradigm that utilizes sememe knowledge in natural languages. We provided experimental results verifying that the proposed SememeSC performs superior to baselines in additive white Gaussian noise (AWGN) and Rayleigh fading channels.
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