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

Abstract

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 paper 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.

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“…In this sense, based on the definitions provided by Gharagozlou et al (2023), the method used for this portion of data extraction is categorized. The initial characteristic of the relation extraction (RE) for this work is that it is a Global RE.…”

“…This indicates that the fulltext reads generate a list as its output from a large amount of text as input. This analysis should produce a list of entity pairings that have a specific semantic relationship (Gharagozlou et al, 2023). For this work, important sentences that could indicate the relationship type are retrieved from the documents to be submitted for semantic analysis.…”

“…The technique chosen RE is pattern-based because of its ability to compare the textual patterns manually delimited. Pattern-based techniques, although unable to identify every pattern, can extract both taxonomic and non-taxonomic relationships between pieces of information(Gharagozlou et al, 2023). Finally, a semi-supervised technique is used in this study to extract semantic relations from the manually labeled samples.…”