# PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

arXiv: https://arxiv.org/abs/2005.06588 · HTML: https://mlsanity.com/paper/2005.06588/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 2 of 2)

- [Improving Persian Relation Extraction Models by Data Augmentation](https://mlsanity.com/paper/2203.15323/) (2022)
- [IHUBERT: Vector-Based Semantic Deduplication and Domain-Balanced Pretraining for Persian Resources](https://mlsanity.com/paper/2606.20089/) (2026)

## Abstract (arXiv)

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.