This repository serves as a curated collection of research papers and notes related to various Natural Language Processing (NLP) tasks, including recommender systems, question answering, text generation, classification, and embeddings. It is primarily intended for researchers and practitioners in the NLP field looking to explore foundational and state-of-the-art techniques.

How It Works

The repository is structured by NLP task categories, with each entry linking to a specific research paper (often via arXiv) and providing a personal note or summary. This organization allows users to quickly navigate and find relevant literature for different areas of NLP research. The collection spans a wide range of publication dates, from 2005 to 2020, showcasing the evolution of techniques.

Quick Start & Requirements

This repository is a collection of links and notes, not a runnable codebase. No installation or specific requirements are needed to browse the content.

Highlighted Details

• Comprehensive coverage of key NLP areas: recommender systems, QA, text generation, classification, embeddings, transformers, sequence-to-sequence models, multi-task learning, named entity recognition, and self-supervised learning.
• Includes links to seminal papers like "Attention Is All You Need" and "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding."
• Features notes on various embedding techniques, including word, sentence, and user embeddings.
• Covers regularization and normalization methods crucial for deep learning optimization.

Maintenance & Community

The repository appears to be a personal collection maintained by xwzhong. No specific community channels or active development indicators are present in the README.

Licensing & Compatibility

The repository itself does not appear to have a specific license. The linked research papers are subject to their respective publication licenses and copyright.

Limitations & Caveats

This is a curated list of papers and personal notes, not an executable library. It does not provide code implementations, benchmarks, or direct access to models. The "notes" are subjective and may not represent a comprehensive analysis of each paper.