[[ Introduction to NLP ]] [[ Getting Started with NLP ]] NLP Models [[ Code Generation ]] [[ Part of Speech ]] Semantic Relations Text Segmentation

Additional Context

This notebook explores the evolving relationship between natural language processing (NLP) and the field of linguistics. As AI models become increasingly sophisticated in their ability to analyze and generate human language, questions arise about the future role of linguists and the potential for AI to automate or augment traditional linguistic analysis.

Key Findings

The research presented in this notebook suggests that while current AI NLP models demonstrate impressive capabilities in tasks such as part-of-speech tagging, semantic analysis, and text segmentation, they are not without limitations. The models require careful prompt engineering and often produce outputs that need human validation and refinement.

Methodology

The experiments documented here involve testing multiple large language models (LLMs) including:

• Google Gemini 1.5: Tested for semantic relationship extraction and POS tagging
• Anthropic Claude: Evaluated for nuanced linguistic analysis
• Cohere: Assessed for text classification and segmentation tasks

Each model was presented with identical text samples and prompts to allow for direct comparison of capabilities and limitations.