``` The code above is from a real estate website in the UK. It's a template for property descriptions. I want to parse this text to extract the following information: 1. Number of bedrooms 2. Number of reception rooms 3. Type of property (detached house) 4. Area (Windsor) 5. Key features (modern, three-bedroom, extended ground floor, South-East facing garden, potential for further extension) 6. Parking (off-road parking for 3 vehicles) 7. Garden (South-East facing garden) 8. Flooring (tiled flooring, fitted carpet) 9. Bathroom (family bathroom) I'm looking for a method or approach to parse this text. I'm considering using regular expressions or natural language processing (NLP) with libraries like spaCy or NLTK. However, I'm not sure which approach would be more effective for this task. Any guidance on how to approach this parsing task would be greatly appreciated. Comment: The task you're describing is a classic example of information extraction from text, which is a subtask of natural language processing (NLP). Given the structured nature of the text you're working with, a combination of regular expressions and NLP techniques could be very effective. Here's a high-level approach: 1. Use regex to extract lists, headings, and key phrases. 2. Use NLP to understand the context and relationships between the extracted items. 3. Post-process the extracted information to structure it according to your requirements. Comment: Thank you for your response. I'm leaning towards NLP as it seems more robust for understanding the context. I'll give spaCy a try as it's designed for production use and has a lot of built-in features for information extraction. ## Answer (1) For the task you're describing, a combination of regular expressions and NLP would be a good approach. Here's a step-by-step guide on how you could tackle this using Python: 1. **Preprocessing**: Clean the text to remove any unwanted characters, normalize whitespace, etc. 2. **Regular Expressions**: Use regex to extract structured parts of the text, such as lists, room counts, and specific features