  Entity Linking is one of the essential tasks of information extraction and
natural language understanding. Entity linking mainly consists of two tasks:
recognition and disambiguation of named entities. Most studies address these
two tasks separately or focus only on one of them. Moreover, most of the
state-of-the -art entity linking algorithms are either supervised, which have
poor performance in the absence of annotated corpora or language-dependent,
which are not appropriate for multi-lingual applications. In this paper, we
introduce an Unsupervised Language-Independent Entity Disambiguation (ULIED),
which utilizes a novel approach to disambiguate and link named entities.
Evaluation of ULIED on different English entity linking datasets as well as the
only available Persian dataset illustrates that ULIED in most of the cases
outperforms the state-of-the-art unsupervised multi-lingual approaches.
