  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 due to exposure to the frequent
occurrences of entities, which have 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. Recently, FarsBase, a Persian knowledge graph,
has been introduced containing almost half a million entities. In this paper,
we propose an unsupervised Persian Entity Linking system, the first entity
linking system specially focused on the Persian language, which utilizes
context-dependent and context-independent features. For this purpose, we also
publish the first entity linking corpus of the Persian language containing
67,595 words that have been crawled from social media texts of some popular
channels in the Telegram messenger. The output of the proposed method is 86.94%
f-score for the Persian language, which is comparable with the similar
state-of-the-art methods in the English language.
