English-Gujarati Medical Domain Parallel Corpora
The dataset consists of bilingual sentence-aligned corpora for the Medical domain from English to Gujarati and vice versa.
Category
Parallel Corpora
Volume
50K+ Corpus
Last Updated
June 2022
Number of participants
200+ people
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About This OTS Dataset
Introduction
Welcome to the English-Gujarati Bilingual Parallel Corpora dataset for the Medical domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Gujarati, providing a valuable resource for developing Medical domain-specific language models and machine translation engines.
Dataset Content
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Extensive Dataset:
Over 50,000 sentences offering a robust dataset for various applications.
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Translator Diversity:
Contributions from more than 200 native translators ensure a wide range of linguistic styles and interpretations.
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Word Count:
Sentences range from 7 to 25 words, suitable for various computational linguistic applications.
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Syntactic Variety:
The corpus encompasses sentences with varying syntactic structures, including simple, compound, and complex sentences.
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Interrogative and Imperative Forms:
The corpus includes sentences in interrogative (question) and imperative (command) forms, reflecting the conversational nature of the Medical industry.
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Affirmative and Negative Statements:
Both affirmative and negative statements are represented in the corpus, ensuring different polarities.
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Passive and Active Voice:
The corpus features sentences written in both active and passive voice, ensuring different perspectives and representations of information.
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Idiomatic Expressions and Figurative Language:
The corpus incorporates idiomatic expressions, metaphors, and figurative language commonly used in the Medical domain.
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Discourse Markers and Connectives:
The corpus includes a wide range of discourse markers and connectives, such as conjunctions, transitional phrases, and logical connectors, which are crucial for capturing the logical flow and coherence of the text.
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Cross Translation:
The dataset includes a cross-translation, where a part of the dataset is translated from English to Gujarati and another portion is translated from Gujarati to English, to improve bi-directional translation capabilities.
Domain Specific Content
This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Medical industry.
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Industry-Tailored Terminology:
The corpus encompasses a comprehensive lexicon of Medical-specific terminology, ranging from technical terms related to anatomy, diseases, and treatments to medical procedures and pharmaceuticals.
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Authentic Industry Expressions:
Beyond technical terminology, the corpus captures the authentic expressions, idioms, and colloquialisms used within the Medical domain.
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Contexts Specific to Shopping Domain:
The corpus encompasses a diverse range of contexts specific to the Medical domain, including patient symptoms, diagnosis, treatment plans, medical research papers, and more.
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Cross-Domain Applicability:
While the primary focus is on the Medical sector, the corpus also includes relevant cross-domain content, such as health, wellness, medical devices, self-care, supplements, etc
Format and Structure:
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Multiple Formats:
Available in Excel format, with the ability to convert to JSON, TMX, XML, XLIFF, XLS, and other industry-standard formats, facilitating ease of use and integration.
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Structure:
It contains information like Serial Number, Unique ID, Source Sentence, Source Sentence Word Count, Target Sentence, and Target Sentence Word Count.
Usage and Application
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Machine Translation:
Develop accurate machine translation engines for medical content localization.
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NLP Applications:
Enabling the creation and improvement of predictive keyboards, spell checkers, grammar checkers, and text/speech understanding systems.
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LLM Training:
Training, fine-tuning, and enhancing bilingual capabilities of LLMs for the Medical domain.
Secure and Ethical Collection
•Our proprietary parallel corpus platform “Yugo” was used throughout the process of this dataset creation.
•Throughout the dataset creation process, the data remained within our secure platform and did not leave our environment, ensuring data security and confidentiality.
•It does not include any personally identifiable information, which makes the dataset safe to use.
•The source or translated content included in the corpus does not infringe upon any copyrights or intellectual property rights. The corpus comprises original content created specifically for this purpose.
Update and Customization
To ensure the continued relevance and effectiveness of this Medical Domain Parallel Corpora Dataset for robust language models and machine translation engines, we are committed to regular updates.
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Annotation:
Various types of annotations like Part-of-speech tagging, Named Entity Recognition (NER), Sentiment Analysis, Intent Classification, Multiple Translation Ranking, or any other application-specific annotations can be made available upon request.
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Classification:
Classification of corpus based on type of sentence, and subdomain can be made available.
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Custom Collection:
Custom collection can be done on specific requirements in any language pair and domain.
License
This Gujarati-English Parallel Corpus dataset for the Medical domain is created by FutureBeeAI and is available for commercial use.
Use Cases
MT Engine
Language model
Predictive keyboards
Spell check
Grammar correction
Text/speech systems
Dataset Sample(s)
SAMPLE
| Source Language | Target Language |
|---|---|
| Smoking and drinking alcohol is injurious to health. | ધૂમ્રપાન અને દારૂ પીવું સ્વાસ્થ્ય માટે હાનિકારક છે. |
| The organs of two brain dead patients were donated on the same day in Surat. | સુરતમાં એક જ દિવસે બે બ્રેનડેડ દર્દીના અંગોનું દાન કરવામાં આવ્યું. |
| The patient underwent a heart transplant at a hospital 273 km in 90 minutes Far away from Ahmedabad . | 90 મિનિટમાં 273 કિ.મી. દૂર અમદાવાદની હોસ્પિટલમાં દર્દીનું હાર્ટ ટ્રાન્સપ્લાન્ટ કરાયું. |
| Swine flu became more deadly than Corona. | કોરોના કરતાં પણ સ્વાઇન ફ્લૂ વધુ ઘાતક બન્યો. |
| The highest number of swine flu cases were reported this year. | આ વર્ષે સ્વાઇન ફ્લૂના સૌથી વધુ કેસ નોધાયા. |
| Gujarat ranks second in the highest number of deaths due to swine flu. | સ્વાઇન ફ્લૂથી સૌથી વધુ મૃત્યુમાં ગુજરાત બીજા સ્થાને. |
| Gujarat reported 1315 cases of swine flu in a month out of which 34 died. | ગુજરાતમાં એક મહિનામાં સ્વાઇન ફ્લૂના ૧૩૧૫ કેસ, જેમાંથી ૩૪ નું મૃત્યુ થયું. |
| Alzheimer's disease, which cripples the elderly even though the body is healthy. | શરીરે સ્વસ્થ હોવા છતાં વૃદ્ધોને પાંગળા બનાવી દેતી બીમારી, અલ્ઝાઈમર. |
| The number of people suffering from Alzheimer's in India is around 3.5 million. | ભારતમાં અલ્ઝાઈમરથી ૫ીડાતા લોકોની સંખ્યા ૩૫ લાખ જેટલી છે. |
| More than two and a half crore people in world suffer from the Sourceoblem of amnesia. | દૂનિયામાં અઢી કરોડથી પણ વધુ લોકો સ્મૃતિભ્રંશની સમસ્યા ભોગવે છે. |
ATTRIBUTES
| target_language | Gujarati |
| source_language | English |
| domain | Medical |
Dataset Details
Dataset type
Text Corpus Data
Volume
50K+ Sentences
Media type
Text
Language pair
English-Gujarati
File Details
Type
Bilingual
Word count
7 to 12 words per asset
Format
XLSX, TMX, XML, XLIFF, XLS
Annotation
NA
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