IN RECENT YEARS, SOCIAL MEDIA DATA HAS EXPONENTIALLY INCREASED, WHICH CAN BE ENUMERATED AS ONE OF THE LARGEST DATA REPOSITORIES IN THE WORLD. A LARGE PORTION OF THIS SOCIAL MEDIA DATA IS NATURAL LANGUAGE TEXT. HOWEVER, THE NATURAL LANGUAGE IS HIGHLY AMBIGUOUS, SPECIFICALLY WITH RESPECT TO THE FREQUENT OCCURRENCES OF ENTITIES, WHICH ARE ADDRESSED BY POLYSEMOUS WORDS OR PHRASES. ENTITY LINKING IS THE TASK OF LINKING THE ENTITY MENTIONS IN THE TEXT TO THEIR CORRESPONDING ENTITIES IN A KNOWLEDGE BASE. MOST OF THE ENTITY LINKING SYSTEMS BEGIN WITH SEARCHING FOR CANDIDATE ENTITIES, AND THEN DISAMBIGUATE THEM TO, FINALLY, CHOOSE THE BEST CANDIDATE. UNFORTUNATELY, DUE TO THE LACK OF A KNOWLEDGE GRAPH, THIS TASK HAD NOT BEEN ABLE TO BE COVERED IN THE PERSIAN LANGUAGE. FORTUNATELY, RECENTLY FARSBASE HAS BEEN INTRODUCED AS A PERSIAN KNOWLEDGE GRAPH WITH ALMOST HALF A MILLION ENTITIES. CORRESPONDINGLY, IN THIS PAPER, WE PROPOSE AN UNSUPERVISED PERSIAN ENTITY LINKING SYSTEM, USING CONTEXT-DEPENDENT AND CONTEXT-INDEPENDENT FEATURES. FOR THIS PURPOSE, WE ALSO PUBLISH THE FIRST ENTITY LINKING CORPUS ON THE PERSIAN LANGUAGE, COMPOSED OF SOCIAL MEDIA TEXTS ON A NUMBER OF POPULAR PERSIAN CHANNELS, IN THE TELEGRAM SOCIAL NETWORK. THE RESULTS PROVE THE HIGHLY EFFICIENT PERFORMANCE OF THE PROPOSED METHOD, WHICH IS COMPARABLE WITH THE CORRESPONDING STATE OF THE ART IN THE ENGLISH LANGUAGE.