This guide introduces Stanford CoreNLP, a Python interface for fundamental NLP tasks such as tokenization, part‑of‑speech tagging, named‑entity recognition, constituency and dependency parsing, showing installation steps, model download links, and example outputs.
This article provides a comprehensive overview of Natural Language Processing, detailing the two main branches—Natural Language Understanding and Generation—along with their sub-modules, typical tasks, implementation approaches, a step‑by‑step NLG pipeline, and a three‑layer analysis framework covering lexical, syntactic, and semantic processing.
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 GAST, a graph‑adaptive semantic transfer framework that combines POS‑based Transformers and hybrid graph attention to improve cross‑domain sentiment analysis, presents related work, details the model architecture, reports extensive experiments showing state‑of‑the‑art results, and discusses future directions.
This article explains how to perform Chinese part-of-speech tagging using the jieba.posseg library in Python, including loading stop words, extracting article content via Newspaper3k, applying precise mode segmentation, filtering, and presenting results in a pandas DataFrame.
