Semantic Relation Extraction: A Review of Approaches, Datasets, and Evaluation Methods With Looking at the Methods and Datasets in the Persian Language

Gharagozlou, Hamid, Mohammadzadeh, Javad, Bastanfard, Azam and Shiry Ghidary, Saeed (2023) Semantic Relation Extraction: A Review of Approaches, Datasets, and Evaluation Methods With Looking at the Methods and Datasets in the Persian Language. ACM Transactions on Asian and Low-Resource Language Information Processing, 22 (7). pp. 1-29. ISSN 2375-4699

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Abstract or description

A large volume of unstructured data, especially text data, is generated and exchanged daily. Consequently, the importance of extracting patterns and discovering knowledge from textual data is significantly increasing. As the task of automatically recognizing the relations between two or more entities, semantic relation extraction has a prominent role in the exploitation of raw text. This article surveys different approaches and types of relation extraction in English and the most prominent proposed methods in Persian. We also introduce, analyze, and compare the most important datasets available for relation extraction in Persian and English. Furthermore, traditional and emerging evaluation metrics for supervised, semi-supervised, and unsupervised methods are described, along with pointers to commonly used performance evaluation datasets. Finally, we briefly describe challenges in extracting relationships in Persian and English and dataset creation challenges.

Item Type:	Article
Uncontrolled Keywords:	Semantic relations, relation extraction, Natural Language Processing (NLP), automatic extraction, Persian text processing, linguistics, dataset, evaluation methods, information extraction
Faculty:	School of Digital, Technologies and Arts > Computer Science, AI and Robotics
Depositing User:	Saeed SHIRY GHIDARY
Date Deposited:	31 May 2024 15:04
Last Modified:	31 May 2024 15:04
URI:	https://eprints.staffs.ac.uk/id/eprint/8294

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