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• For full Data Science tasks, materials, etc. please check Data Science repository.

• For Machine Learning algorithms please check Machine Learning repository.

• For Deep Learning algorithms please check Deep Learning repository.

• For Computer Vision please check Computer Vision repository.

• NLP-progress - Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
  • :octocat: Tracking Progress in Natural Language Processing

💠 Natural Language Processing Tasks

Folders with all materials for specific task/domain

• Data Analysis
• Knowledge Graph
• Models and Algorithms
• Ontologies
• Question Answering System
• Search Engine
• Sentiment Analysis
• Shallow Discourse Parsing
• Text Classification
• Topic Modeling
• Word Embedings

🎓 Courses

• Natural Language Processing - Stanford University| Dan Jurafsky, Christopher
• Natural Language Processing with Deep Learning (Stanford CS224N)
  • Course website
  • Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2021
  • Natural Language Processing with Dan Jurafsky and Chris Manning, 2012 Stanford Online

  Modern NLP techniques from recurrent neural networks and word embeddings to transformers and self-attention. Covers applied topics like questions answering and text generation.

• Stanford CS224U: Natural Language Understanding | Spring 2019
• Natural Language Processing | University of Michigan
• :octocat: A Code-First Introduction to NLP course by fast.ai
• From Languages to Information by Stanford University
• Deep Learning for Natural Language Processing by University of Oxford
• Natural Language Processing by University of Washington
• Natural Language Processing by Yandex Data School
  • NLP Course | For You

  This is an extension to the (ML for) Natural Language Processing course teached at the Yandex School of Data Analysis (YSDA). For now, only part of the topics is likely to be covered here.

• Natural Language Processing by National Research University Higher School of Economics (via Coursera)
• Applied Natural Language Processing by UC Berkeley
• Advanced Methods in Natural Language Processing by Tel Aviv University
• Text Retrieval and Search Engines [FULL COURSE] | UIUC

  This course will cover search engine technologies, which play an important role in any data mining applications involving text data for two reasons. First, while the raw data may be large for any particular problem, it is often a relatively small subset of the data that are relevant, and a search engine is an essential tool for quickly discovering a small subset of relevant text data in a large text collection. Second, search engines are needed to help analysts interpret any patterns discovered in the data by allowing them to examine the relevant original text data to make sense of any discovered pattern. You will learn the basic concepts, principles, and the major techniques in text retrieval, which is the underlying science of search engines.

• Text Mining and Analytics [FULL COURSE] | UIUC
• Mining Massive Datasets - Stanford University [FULL COURSE]
• Coursera:
  • Специализация Обработка текстов, написанных на естественных языках
  • Text Mining (Анализ текстовой информации и аналитика)
• LinkedIn Learning:
  • Advanced NLP with Python for Machine Learning
    • Folder with code
• :octocat: Матеріали для курсу NLP в Проджекторі
• Advaced NLP with spaCy
• fast.ai course: A Code-First Introduction to Natural Language Processing
  • :octocat: A Code-First Intro to Natural Language Processing on GitHub :octocat:
• :octocat: CS 4650 and 7650 - Course materials for Georgia Tech CS 4650 and 7650, "Natural Language"

📚 Books

• A lot of NLP books (Natural Language Processing)
• Advanced Natural Language Processing with TensorFlow 2, published by Packt
• Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit, Steven Bird, Ewan Klein, and Edward Loper
• Speech and Language Processing (3rd ed. draft), Dan Jurafsky and James H. Martin
• Neural Network Methods for Natural Language Processing
• Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit
  • NLTK_book on GitHub
• Practical Natural Language Processing

  • Official Repository for 'Practical Natural Language Processing' by O'Reilly Media

• Natural Language Processing Notebooks

  • Available as a Book: NLP in Python - Quickstart Guide

🎥 YouTube

🖥️ Web Sites

• Natural Language Processing on Papers with Code
• Chris McCormick Blog
  • ChrisMcCormickAI YouTube Channel
• The NLP Index
• Sebastian Ruder

  • I'm a research scientist at Google. I blog about natural language processing and machine learning.

• :octocat: Software Engineering Blogs

  • A curated list of engineering blogs

• The Gradient

  • The Gradient is an organization with the missions of making it easier for anyone to learn about AI and of facilitating discussion within the AI community. We were founded in 2017 by a group of students and researchers at the Stanford AI Lab.

📰 Atricles

• Карьера в IT: NLP Engineer и NLP Researcher

:octocat: GitHub Repositories

🗣️ Conferences

• NeurIPS - Neural Information Processing Systems
• ACL - ACL Home Association for Computational Linguistics
  • ACL Anthology - The ACL Anthology currently hosts 80890 papers on the study of computational linguistics and natural language processing.

🛠️ Tools

NLP libraries, frameworks, modules

👩🏻‍🏫 Tutorials

🔝 Leaderboard for NLP

• ExplainaBoard: An Explainable Leaderboard for NLP

💠 Deep Learning architectures for NLP

• Introduction to Deep Learning for Natural Language Processing
• Deep Learning architectures for NLP - Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP

🔹 100 Must-Read NLP Papers

🔹 Sci-Hub(Papers)

🔹 Stanford, NLP Seminar Schedule

🔹 CS224n: Natural Language Processing with Deep Learning

🔹 CIS 700-008 - Interactive Fiction and Text Generation

🔹 Harvard NLP

🔹 The Illustrated Transformer

🔺 Projects

• Spam Detection
• Text Generator
• Question Answering System using BiDAF Model on SQuAD

Question Answering System using BiDAF Model on SQuAD

Implemented a Bidirectional Attention Flow neural network as a baseline on SQuAD, improving Chris Chute's model implementation, adding word-character inputs as described in the original paper and improving GauthierDmns' code.

• Project Repository
• Paper
• Used Articles
• Useful Articles
• Useful Links

Useful Articles

• Interpretation of Natural Language Rules in Conversational Machine Reading
• Skip-Thought Vectors, Article

Some Concepts

• Selectional Preference - (Katz and Fodor, 1963; Wilks, 1975; Resnik, 1993) are the tendency for a word to semantically select or constrain which other words may appear in a direct syntactic relation with it." In case this selection is expressed in binary term (allowed/not-allowed), it is also called selectional restriction (Séaghdha and Korhonen, 2014). SP can be contrasted with verb subcategorization "with subcategorization describing the syntactic arguments taken by a verb, and selectional preferences describing the semantic preferences verbs have for their arguments" (Van de Cruys et al., 2012)
  • Selectional preference, Natural Language Understanding Wiki
• Selectional Restrictions -
  • Selectional Restrictions, Jurafsky