Text Data Collection

Definition

Text data collection is the process of gathering written language from sources such as books, websites, or chat logs for use in AI training.

Purpose

The purpose is to create corpora for NLP and LLM development.

Importance

• Provides raw material for language models.
• Raises copyright and licensing issues.
• Data diversity influences fairness and accuracy.
• Must filter harmful or irrelevant content.

How It Works

1. Identify text sources (web, documents, transcripts).
2. Crawl or scrape text with permission.
3. Clean and normalize content.
4. Store with metadata for traceability.
5. Use in pre-training or fine-tuning.

Examples (Real World)

• Common Crawl: large web corpus.
• Wikipedia dumps: structured text dataset.
• BooksCorpus: used for training BERT.

References / Further Reading

• Common Crawl Foundation.
• Jurafsky & Martin. Speech and Language Processing.
• ISO/IEC TR 20547-5: Big Data Reference Architecture.