Nepali Health Q&A Corpus by Kaggle | Healthcare

About this Dataset

Nepali Health Q&A Corpus

Investigating Cultural Influences

By Huggingface Hub [source]

About this dataset

This dataset is a collection of translated Nepali health-related questions and responses. It contains contextual information to help readers understand the environment in which the question was asked, as well as the response given to it. This unique corpus gives us insight into the cultural factors that affect how questions are asked and answered by looking at a different language and culture from our own. These responses can give us valuable insights into how people approach medical information around mental health, illness, disease, and other topics in Nepal. By exploring this data we can gain vital understanding of how culture shapes beliefs and knowledge surrounding our collective understanding of health

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How to use the dataset

This dataset contains translated pairs of medical-related questions and responses in the Nepalese language. The primary purpose of this dataset is to provide a rich source of data for health-related question answering systems that are able to understand and respond to questions accurately.

The data consists of two columns: Translated_Context and Translated_Response. The translated context column provides an English translation for each Nepali medical question, while the translated response column contains answers related to the posted question in English.

In order to use this dataset, you should first familiarize yourself with basic medical terms and concepts in both English as well as Nepali. This will make it easier for you to gain a better understanding of the questions and answers provided by this dataset. Furthermore, it will also allow you to get more out of your analysis on health-related cultural factors through exploring different contexts surrounding these questions such as public health policy or internet trends etc..

Once familiarized with general concepts, you can begin exploring individual records in this dataset by reading through both the Translated_Context and Translated_Response fields. Here, try analyzing cultural implications behind specific phrases used within each response or take note any patterns that appear between multiple records which could signal certain individuals' approach towards broadly accepted healthcare advice or practices available within a particular area etc.. Additionally, consider leveraging machine learning techniques like Natural Language Processing (NLP) for deeper sentiment analysis beyond just simply extracting key phrases from text strings present here so as to gain more actionable insight from your explorations about potential trends lurings due recent public health policies or shifting online discussions around particular conditions etc..
Finally, examining headlines about current countries milestones regarding developing healthcare infrastructure might bring useful contextual knowledge that helps you explore any micro-level changes within the country's overall consensus on otherwise broad topics like prevention methods from certain illnesses line malaria etc.

Research Ideas

Training Machine Learning models to generate culturally sensitive responses to Nepali health-related questions.

Analyzing the cultural nuances and connotations of different health queries in the Nepali language based on their responses.

Comparing automated translations of English queries with their Nepali translations regarding accuracy, precision, and tone in order to understand how they may differ across cultures

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv

Column name	Description
Translated_Context	The translated context of the question. (String)
Translated_Response	The translated response to the question. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.

Tables

Train

@kaggle.thedevastator_nepali_health_q_a_corpus.train

7.34 MB
9386 rows
2 columns


CREATE TABLE train (
  "translated_context" VARCHAR,
  "translated_response" VARCHAR
);