Scottish Text Files

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Project Overview:

Objective

Our recent project, “Scottish Text Files” aimed to create a comprehensive dataset of Scottish text files. This dataset serves as a vital resource for training machine learning models in natural language processing, particularly for applications requiring an understanding of Scottish dialects and cultural contexts.

Scope

This initiative involved gathering and annotating a wide range of text files, including literary works, local news articles, and transcriptions of spoken Scottish dialects. The project’s scope extended to sourcing materials from both online repositories and contributions from local Scottish writers and speakers.

Sources

The project included a variety of text types, such as literary works, historical documents, folk tales, academic papers, and modern digital content (blogs, social media posts, etc.).
There was a comprehensive coverage of texts in different Scottish dialects and languages, particularly Scots and Scottish Gaelic, alongside English texts that held significant cultural relevance to Scotland.
The collection comprised texts from various time periods, ranging from ancient and medieval Scottish literature to contemporary writings.
We successfully collected a diverse and comprehensive set of texts, successfully generating a rich linguistic and cultural representation of Scotland across different dialects, languages, and historical periods.

Data Collection Metrics

Total Text Files Collected: 20,000
Online Repositories: 8,000 text files from online sources, including public domain literary works and digital archives.
Local Contributions: 12,000 text files, including contemporary writings, transcripts of spoken language, and local news articles.

Annotation Process

Stages

Content Categorization: Each text file was annotated based on content type (e.g., literature, news, dialogue) and linguistic features specific to the Scottish context.
Metadata Annotation: We recorded metadata for each file, such as the source, authorship (if available), and publication date.

Annotation Metrics

Text Files with Content Labels: 20,000
Metadata Annotations: 15,000

Quality Assurance

Stages

Annotation Verification: A team of language experts specializing in Scottish dialects and literature reviewed the annotations for accuracy and cultural relevance.
Data Quality Control: We rigorously filtered out irrelevant or low-quality text files.
Data Security: Ensured compliance with data protection laws and ethical standards for text data.

QA Metrics

Reviewed and Validated Annotations: 5,000 files
Data Cleansing: Removal and refinement of the dataset for maximum relevance and quality.

Conclusion

The “Scottish Text Files” project stands as a testament to our commitment to providing high-quality, culturally nuanced datasets for the burgeoning field of machine learning. With a robust dataset of Scottish text files, we empower developers and researchers to create more inclusive and region-specific AI applications. This project not only enhances language model accuracy but also bridges cultural gaps in the digital world.