  Relation extraction is the task of extracting semantic relations between
entities in a sentence. It is an essential part of some natural language
processing tasks such as information extraction, knowledge extraction, and
knowledge base population. The main motivations of this research stem from a
lack of a dataset for relation extraction in the Persian language as well as
the necessity of extracting knowledge from the growing big-data in the Persian
language for different applications. In this paper, we present "PERLEX" as the
first Persian dataset for relation extraction, which is an expert-translated
version of the "Semeval-2010-Task-8" dataset. Moreover, this paper addresses
Persian relation extraction utilizing state-of-the-art language-agnostic
algorithms. We employ six different models for relation extraction on the
proposed bilingual dataset, including a non-neural model (as the baseline),
three neural models, and two deep learning models fed by multilingual-BERT
contextual word representations. The experiments result in the maximum f-score
77.66% (provided by BERTEM-MTB method) as the state-of-the-art of relation
extraction in the Persian language.
