awesome-nlp README

The project's README from github.com/keon/awesome-nlp. As one markdown file: README.md.

awesome-nlp

Awesome


Sponsored by Atlas Cloud

Atlas Cloud

AI API aggregation platform with an OpenAI-compatible LLM endpoint for NLP tasks such as translation, summarization, multilingual generation, and structured extraction.


A curated list of resources dedicated to Natural Language Processing

Please read the contribution guidelines before contributing. Please add your favourite NLP resource by raising a pull request

Scope

This list covers natural language processing — linguistic analysis, multilingual tooling, classical and neural methods, datasets, and evaluation. Large language models are included only where they advance or evaluate a core NLP task or capability (tokenization, multilinguality, MT, summarization, NER, QA, factuality, probing, distillation). General-purpose chatbots, agent frameworks, prompt-template repositories, code-generation tools, and RAG application starter kits live in other lists — see See Also.

Contents

Where to follow current NLP research:

Historical highlights

Prominent NLP Research Labs

Back to Top

Tutorials

Back to Top

Reading Content

General Machine Learning

Introductions and Guides to NLP

Blogs and Newsletters

Videos and Online Courses

Back to Top

Books

Libraries

Back to Top

awesome-nlp (keon/awesome-nlp)

books
Searching Database…

What is this?A curated list of resources dedicated to Natural Language Processing — research hubs, tutorials, courses, books, libraries, datasets, and task methods, collected as links with one-line descriptions.
Is this a tool I install, or just a page of links?Neither — it's an awesome-style catalog you read on GitHub. Every entry points to an external resource (a course, book, library, dataset, or paper hub) with a short description.
Which NLP tasks does the Tasks and Methods section cover?Text embeddings; tokenization and segmentation; POS tagging and dependency parsing; named entity recognition; coreference resolution; text classification and sentiment; topic modeling; summarization; machine translation; question answering; and information extraction beyond NER.
Are any of the books or courses free?Yes — Dan Jurafsky's Speech and Language Processing and Jacob Eisenstein's NLP notes are both free, and courses like Advanced NLP with spaCy and Cohere LLM University are free too.
Does it go beyond classic NLP into language-model research?Yes — sections cover pretraining and adaptation, instruction tuning and preference optimization, efficient and small models, reasoning and test-time compute, long context, factuality and hallucination, and bias, fairness and safety in NLP.
Creator
keon
Size
0.7 MB
Stars
19.1K
Updated
CommitOct 12
Age
10y
Commits · 90 Days