Introduction
Welcome to the English-Urdu Bilingual Parallel Corpora dataset for the Medical domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Urdu, providing a valuable resource for developing Medical domain-specific language models and machine translation engines.
Dataset Content
•Volume and Diversity:
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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 Urdu and another portion is translated from Urdu 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 Urdu-English Parallel Corpus dataset for the Medical domain is created by FutureBeeAI and is available for commercial use.
