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

• URL: http://arxiv.org/abs/2005.06588v1
• Date: Wed, 13 May 2020 21:06:59 GMT
• Title: PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction
• Authors: Majid Asgari-Bidhendi, Mehrdad Nasser, Behrooz Janfada, Behrouz Minaei-Bidgoli
• Abstract summary: "PERLEX" is the first dataset for relation extraction in the Persian language. We employ six different models for relation extraction on the proposed bilingual dataset. Experiments result in the maximum f-score 77.66% as the state-of-the-art of relation extraction in the Persian language.
• Score: 6.10917825357379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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