Welsh Text Files

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Project Overview:

Objective

Our “Welsh Text Files” project aims to create a comprehensive dataset of Welsh text, focusing on linguistic diversity and richness. This dataset will be a valuable resource for training and improving natural language processing (NLP) models, helping them to better understand and generate Welsh text.

Scope

Our project involves the careful collection of Welsh text data from a range of sources. After collecting this data, we thoroughly annotate it to add important contextual information and linguistic details. Consequently, this enriched dataset addresses the growing need for Welsh language support in NLP applications, including machine translation, sentiment analysis, and chatbots.

Sources

The project covered a range of text types, including literary works, news articles, historical documents, educational materials, and everyday communication like emails and social media posts.
Special emphasis was placed on covering various dialects and historical stages of the Welsh language to ensure rich linguistic diversity.
We collected a comprehensive dataset from these diverse text types, which allowed us to generate a nuanced understanding of the Welsh language across various dialects and historical periods.

Data Collection Metrics

Web Scraping: 30,000 files
User Contributions: 15,000 files
Public Domain Texts: 5,000 files

Annotation Process

Stages

Linguistic Annotation: Each text file is carefully annotated with linguistic features, such as part-of-speech tagging, named entity recognition, and sentiment analysis. Consequently, these detailed annotations provide a solid foundation for various natural language processing (NLP) tasks, which significantly enhance the dataset’s overall value.
Contextual Enrichment: To further increase the utility of the dataset in NLP tasks, we have provided additional context. This includes context-based word embeddings and semantic labeling, which help capture the nuances and relationships within the text. These enhancements will improve the performance of models by providing richer and more detailed information about the words and their meanings.

Annotation Metrics

Linguistic Annotations: 50,000 files
Contextual Enrichment: 40,000 files

Quality Assurance

Stages

Annotation Verification: Our team of linguists and NLP experts thoroughly review and verify the accuracy of linguistic annotations, ensuring high-quality data. Furthermore, we ensure that all annotations meet the required standards.
Data Consistency: We maintain data consistency by adhering to linguistic standards and guidelines specific to Welsh language processing. This practice guarantees uniformity and reliability across all data sets.
Data Security: Our commitment to data security includes protecting user-contributed content and respecting privacy regulations. In addition, we implement strict measures to safeguard all user information and maintain trust.

QA Metrics

Annotation Verification Cases: 2,000

Conclusion

The “Welsh Text Files” dataset represents a significant contribution to the field of natural language processing. Specifically tailored to the Welsh language. With a substantial volume of meticulously collected and annotated text files. This dataset empowers the development of advanced NLP models that can comprehend and generate Welsh text with remarkable accuracy. It supports a wide range of applications. From improving machine translation services to enabling sentiment analysis in Welsh, and ultimately. Promoting the preservation and enrichment of the Welsh language in the digital landscape.