This tutorial introduces AIGC concepts and walks through practical implementations of tokenization, part‑of‑speech tagging, and named entity recognition using the Transformers library, NLTK, and spaCy on Google Colab, complete with code snippets and visual results.
This article introduces five widely used Python libraries—Pandas, NumPy, NLTK, Scikit-learn, and Matplotlib—explaining their core functionalities for data cleaning, statistical analysis, natural language processing, machine‑learning modeling, and visualization, and provides practical code snippets for each.
This guide shows how to use Python libraries such as docx, jieba, NLTK, and openpyxl to read a Word file, perform tokenization, compute word frequencies, assign part‑of‑speech tags, and export the results into an Excel spreadsheet, including troubleshooting tips for common errors.
This tutorial introduces natural language processing (NLP) with Python, covering the basics of NLP, popular libraries, NLTK installation, text tokenization, stop‑word removal, stemming, lemmatization, synonym/antonym handling, and multilingual support, all illustrated with clear code examples and visual guides.
