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.

IN THIS PAPER WE USE DISTANT SUPERVISION FOR THE TASK OF RELATION EXTRACTION FROM A LARGE CORPUS IN THE PERSIAN LANGUAGE. THERE ARE SUPERVISED AND UNSUPERVISED METHODS FOR RELATION EXTRACTION FROM TEXT. IN SUPERVISED RELATION EXTRACTION WE USE HAND LABELED CORPORA. THIS METHOD SUFFERS FROM DOMAIN DEPENDENCIES AND THE DIFFICULTIES OF LABELING THE TEXT. IN UNSUPERVISED METHOD, WE USE LARGE CORPORA WITHOUT HAVING TO LABEL THEM BUT RELATIONS EXTRACTED USING THIS METHOD CANNOT BE USED TO POPULATE KNOWLEDGE BASES. DISTANT SUPERVISION TAKES ADVANTAGE OF LARGE CORPORA WITHOUT SUFFERING FROM DOMAIN DEPENDENCIES AND CAN POPULATE KNOWLEDGE BASES. FOR OUR EXPERIMENT WE USE FarsBase, A KNOWLEDGE BASE CONTAINING MILLIONS OF RELATION INSTANCES, AND ALIGN ENTITIES IN 630000 PERSIAN WIKIPEDIA ARTICLES TO THESE RELATION INSTANCES AND CREATE A DISTANTLY SUPERVISED DATASET. WE THEN EXTRACT NEW RELATION INSTANCES USING PIECEWISE CONVOLUTIONAL NEURAL NETWORKS AND COMPARE THE RESULTS WITH THE BASELINE MODEL THAT USES MANUALLY EXTRACTED FEATURES.