Chinese & English & Tibetan & Uyghur Language Dataset

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Project Overview:

Objective

mission was to curate a comprehensive dataset encompassing Chinese, English, Tibetan, and Uyghur languages for machine learning model training. This multi-lingual dataset serves as a valuable resource for a wide range of applications, including natural language processing, sentiment analysis, and machine translation.

Scope

Our project involved the meticulous collection of Multi-Language Dataset data in Chinese, English, Tibetan, and Uyghur languages from diverse sources, followed by rigorous annotation to ensure data accuracy and usability.

Sources

Web Crawling: We employed web crawling techniques to gather a vast amount of text data from websites, forums, and articles in the target languages.
Public Datasets: Accessing publicly available text datasets allowed us to augment our collection with high-quality content.
Collaboration: Collaborative efforts with linguists and native speakers aided in the acquisition of authentic and contextually relevant data.

Data Collection Metrics

Total Text Data Collected: Over 1.5 million documents
Chinese Texts: 600,000 documents
English Texts: 500,000 documents
Tibetan Texts: 200,000 documents
Uyghur Texts: 200,000 documents

Annotation Process

Stages

Language Labeling: Each document was meticulously annotated with its respective language label.
Sentiment Analysis: We conducted sentiment analysis to provide insights into the emotional tone of the text.
Topic Categorization: Documents were categorized into various topics to enhance the dataset’s usability.

Annotation Metrics

Language Labels: 1.5 million documents
Sentiment Analysis Scores: 1.2 million documents
Topic Categories: 800,000 documents

Quality Assurance

Stages

Expert Review: We engaged linguists and native speakers to validate the accuracy of language labeling and sentiment analysis.
Data Cleansing: Rigorous quality control ensured the removal of irrelevant or low-quality texts.
Data Security: Stringent data security protocols were in place to protect sensitive linguistic content and user privacy.

QA Metrics

Expert Validation Cases: 5,000 documents
Data Cleansing: 10,000 documents

Conclusion

The “Chinese & English & Tibetan & Uyghur Language Dataset” represents a significant milestone in linguistic data curation. This diverse and extensive dataset serves as a valuable asset for the development of machine learning models and applications that require multilingual text analysis. Its accuracy, breadth, and depth make it an indispensable resource for researchers and developers in the field of natural language processing.