A guide to natural language processing with Python using spaCy

Natural language processing (NLP) is an AI subfield focused on enabling computers to understand and generate human language, with applications such as speech recognition and sentiment analysis. The article explores NLP using spaCy, an open-source Python library that facilitates tasks like tokenization, lemmatization, part-of-speech tagging, and named entity recognition through a pipeline of specialized components. SpaCy's efficiency makes it suitable for large-scale NLP tasks, and it leverages pre-trained models like en_core_web_sm, which are trained on web excerpts, to perform various language processing functions. The article also touches on the importance of model quality, which depends on dataset size, and suggests that pre-trained models are generally adequate, though domain-specific applications may require custom training. Additionally, the text briefly mentions ChatGPT, a language model capable of generating human-like text, as an example of NLP in action.