> [!Robot] This is AI-generated text from Claud 3.5 Sonnet: Here are some resources I'd recommend for deepening your understanding of spaCy and its advanced features: 1. Official spaCy Documentation: - https://spacy.io/usage This is always the best place to start. The documentation is comprehensive and includes tutorials, guides, and API references. 2. spaCy Course: - https://course.spacy.io/ This free interactive course covers everything from basics to advanced topics like custom pipelines and training models. 3. Explosion AI YouTube Channel: - https://www.youtube.com/@ExplosionAI Explosion AI, the company behind spaCy, has a YouTube channel with tutorials and talks about NLP and spaCy. 4. "Natural Language Processing with Python and spaCy" by Yuli Vasiliev: - This book provides a comprehensive guide to using spaCy for various NLP tasks. 5. Real Python Tutorial on spaCy: - https://realpython.com/spacy-python-natural-language-processing/ This tutorial offers a practical introduction to using spaCy for NLP tasks. 6. spaCy Universe: - https://spacy.io/universe A collection of libraries, extensions, and projects that work with spaCy. 7. "Advanced NLP with spaCy" by Ines Montani: - https://course.spacy.io/en/ Another free course, more advanced than the basic spaCy course. For your specific task of complex information extraction: 1. Focus on the Rule-based Matching section in the spaCy documentation: - https://spacy.io/usage/rule-based-matching 2. Learn about Training a New Entity Type: - https://spacy.io/usage/training#ner 3. Explore the EntityRuler component: - https://spacy.io/api/entityruler Remember, while pattern-based approaches (like the one we used) are a good starting point, for more complex or ambiguous cases, you might need to look into training custom models. This involves creating annotated training data and using spaCy's model training capabilities. As you delve deeper, you'll find that spaCy offers a powerful blend of rule-based and statistical approaches, allowing you to tackle even very complex information extraction tasks. Don't hesitate to ask if you need clarification on any concepts as you explore these resources! --- **Relates to**: