PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction [article]

Majid Asgari-Bidhendi, Mehrdad Nasser, Behrooz Janfada, Behrouz Minaei-Bidgoli
2020 arXiv   pre-print
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
more » ... aper, 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.
arXiv:2005.06588v1 fatcat:c25gjciuqrg7pg45p56nqc7kvy