{"title":"Sememe-Based Semantic Communications","authors":"Tuna Ozates;Uras Kargı;Aykut Koç","doi":"10.1109/LCOMM.2024.3450082","DOIUrl":null,"url":null,"abstract":"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 letter 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. Codes and data are available at \n<uri>https://github.com/koc-lab/sememesc</uri>\n.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"28 10","pages":"2308-2312"},"PeriodicalIF":3.7000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Communications Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10648841/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
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 letter 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. Codes and data are available at
https://github.com/koc-lab/sememesc
.
期刊介绍:
The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.