Bangla-REX: A distinct dataset for Bangla relation extraction

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-03-20 DOI:10.1016/j.dib.2025.111480

Nishat Tasnim , Asraf Ullah Rahat , Tanjim Taharat Aurpa , Md Musfique Anwar

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引用次数: 0

Abstract

Bangla REX introduces a significant dataset designed specifically for relation extraction tasks in the Bangla language, addressing a critical lack of resources in this domain. To the best of our knowledge, this dataset fills an important gap by providing comprehensive support for advanced relation extraction methodologies. The dataset comprises 90,441 text entries, available in structured formats. Alongside this, a Bangla Knowledge Base (KB) containing 63,256 entries has been developed to automate the corpus annotation with relation tags.

The dataset is developed to support the accurate extraction and classification of various semantic relations within Bangla textual data. It includes categories such as movie actors, place of birth, movie directors, writers, places of death, company locations, and founders. Bangla REX is a valuable resource for researchers and practitioners in natural language processing, offering standardized benchmarks and detailed annotations to advance relation extraction techniques for Bangla. It aims to foster research and enable practical applications, contributing to the development of Bangla NLP and multilingual information processing.

The dataset is compiled from reliable sources, including Bangla Wikipedia and Wikidata, and has been annotated to ensure accuracy and relevance. Each relation category contains numerous labeled examples, providing a strong foundation for training and evaluating relation extraction models. Bangla REX seeks to promote innovation in NLP research and support the development of tools tailored to the Bangla language.

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.