This project provides a foundational deep learning framework for implementing various NLP tasks including word embeddings, language modeling, Chinese word segmentation, part-of-speech tagging, dependency parsing, semantic role labeling, relation extraction, and text classification. It implements classical models like Word2Vec with hierarchical softmax and negative sampling, and is designed for educational and experimental purposes rather than production use.