This repository serves as a curated collection of natural language processing (NLP) research papers, accompanied by reading notes, model implementations, and data processing scripts. It targets NLP researchers, engineers, and students seeking to deepen their understanding of foundational and state-of-the-art NLP techniques. The project offers dual implementations (TensorFlow and PyTorch) for many models, facilitating cross-framework learning and experimentation.

How It Works

The project organizes papers chronologically and by topic, providing a structured learning path. For key papers, detailed reading notes are included, summarizing core concepts, algorithms, and their advantages. The repository also links to separate repositories for specific sub-projects like text similarity and dialogue systems, indicating a modular approach to managing diverse NLP research areas. A search tool is provided for efficient navigation within the extensive paper list.

Quick Start & Requirements

• Install/Run: Primarily Python-based. The search_kits.py script can be run with python3 search_kits.py Contents.
• Prerequisites: Python 3.x. Specific model implementations may have additional dependencies (e.g., TensorFlow, PyTorch, specific libraries).
• Resources: Varies by paper and implementation; no specific system requirements are listed for the repository itself.
• Links: Blog, Paper Support Repo

Highlighted Details

• Comprehensive coverage of NLP topics including large models, clustering, dialogue systems, graph algorithms, and more.
• Dual TensorFlow and PyTorch implementations for many models.
• Includes reading notes for seminal papers like PageRank, TextRank, Word2Vec, BERT, and Transformer.
• Provides a search utility for easy navigation of the paper collection.

Maintenance & Community

The repository is actively maintained by DengBoCong, with a clear indication that Pull Requests are welcome. Links to social media (Zhihu) are provided for community engagement.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the README. Compatibility for commercial use or closed-source linking would require clarification of the license.

Limitations & Caveats

The repository is a collection of papers and code snippets, not a unified framework. Users need to manage dependencies for individual model implementations. The primary focus is on research and learning, rather than production-ready tools.