How do you handle large datasets in Python for NLP tasks?
Handling large datasets in Python for natural language processing (NLP) tasks can be quite challenging due to memory constraints and processing power requirements. NLP tasks involve the analysis and manipulation of human language data, which can be voluminous and complex. Python, being a high-level programming language, offers a range of libraries and tools designed for efficient data engineering and can be leveraged to manage and process large datasets effectively. Whether you're working on text classification, sentiment analysis, or language translation, understanding how to handle big data can significantly impact the performance and scalability of your NLP applications.