BanglaHealth: A Bengali paraphrase dataset on health domain

In the landscape of natural language processing (NLP) research, the availability of comprehensive datasets plays a pivotal role in advancing various tasks, including paraphrasing. However, for languages such as Bengali, the availability of such datasets remains limited, particularly in specialized domains like health. Recognizing this gap, this study endeavours to address the scarcity of resources by presenting a novel Bengali paraphrasing dataset specifically tailored to the health domain. The dataset construction process involved sourcing sentences from Bengali newspapers, focusing on health-related content. Due to the dearth of existing datasets in Bengali and the specialized nature of paraphrasing, particularly in the health domain, this endeavour necessitated the development of a unique methodology. This methodology included the development of a script for data extraction, pre-processing, translation of Bengali sentences into English, paraphrasing of the English sentences, and subsequent translation back into Bengali to generate paraphrased versions of the original sentences. A user study involving 100 participants was conducted to evaluate 500 sample Bengali paraphrased sentences to identify the most suitable library (<i>VamsiT5 Paws</i>) for creating the paraphrasing dataset. As such, a total of 200,000 sentences were extracted and paraphrased in this process. This dataset holds significant potential to foster advancements in paraphrasing research that facilitate the development of language models; and ultimately contribute to the broader goal of enhancing NLP capabilities in Bengali, particularly in specialized domains like health.

Authors

Institutions

Publication Details

Journal
Data in Brief
Published
2025-05-26
DOI
https://doi.org/10.1016/j.dib.2025.111699
Citations
1
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

BanglaHealth: A Bengali paraphrase dataset on health domain

Muhammad Nazrul Islam, Faisal Ibn Aziz
1 citations
Data in Brief
Topic Modeling
2.48
article

BanglaHealth: A Bengali paraphrase dataset on health domain

Muhammad Nazrul Islam, Faisal Ibn Aziz
article en
1 citations

Abstract

In the landscape of natural language processing (NLP) research, the availability of comprehensive datasets plays a pivotal role in advancing various tasks, including paraphrasing. However, for languages such as Bengali, the availability of such datasets remains limited, particularly in specialized domains like health. Recognizing this gap, this study endeavours to address the scarcity of resources by presenting a novel Bengali paraphrasing dataset specifically tailored to the health domain. The dataset construction process involved sourcing sentences from Bengali newspapers, focusing on health-related content. Due to the dearth of existing datasets in Bengali and the specialized nature of paraphrasing, particularly in the health domain, this endeavour necessitated the development of a unique methodology. This methodology included the development of a script for data extraction, pre-processing, translation of Bengali sentences into English, paraphrasing of the English sentences, and subsequent translation back into Bengali to generate paraphrased versions of the original sentences. A user study involving 100 participants was conducted to evaluate 500 sample Bengali paraphrased sentences to identify the most suitable library (<i>VamsiT5 Paws</i>) for creating the paraphrasing dataset. As such, a total of 200,000 sentences were extracted and paraphrased in this process. This dataset holds significant potential to foster advancements in paraphrasing research that facilitate the development of language models; and ultimately contribute to the broader goal of enhancing NLP capabilities in Bengali, particularly in specialized domains like health.

Data in BriefVol. 61
Bangladesh University (BD)
Openalex Percentile: Top 10%
Topic Modeling
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

BanglaHealth: A Bengali paraphrase dataset on health domain — Muhammad Nazrul Islam, Faisal Ibn Aziz · Data in Brief (2025) | TGRS Research Map | TGRS