Table of Contents
- Talks / Lectures
- Frameworks / Models
- Blog Posts
- Word Embeddings / Word Vectors
- NLP with Deep Learning / CS224N from Stanford (Winter 2019)
- Neural Networks for NLP from Carnegie Mellon University
- Deep Learning for Natural Language Processing from University of Oxford and DeepMind
- Deep Learning with Text: Natural Language Processing (Almost) from Scratch with Python and spaCy by Patrick Harrison and Matthew Honnibal
- Neural Network Methods in Natural Language Processing by Yoav Goldberg and Graeme Hirst
- Deep Learning in Natural Language Processing by Li Deng and Yang Liu
- Natural Language Processing in Action by Hobson Lane, Cole Howard, and Hannes Hapke
- Deep Learning: Natural Language Processing in Python by The LazyProgrammer (Kindle only)
- Applied Natural Language Processing with Python by Taweh Beysolow II
- Deep Learning Cookbook by Douwe Osinga
- Deep Learning for Natural Language Processing: Creating Neural Networks with Python by Palash Goyal, Sumit Pandey, Karan Jain
- Machine Learning for Text by Charu C. Aggarwal
- Natural Language Processing with TensorFlow by Thushan Ganegedara
- fastText Quick Start Guide: Get started with Facebook's library for text representation and classification
- Hands-On Natural Language Processing with Python
- Deep Learning for Natural Language Processing (without Magic)
- A Primer on Neural Network Models for Natural Language Processing
- Deep Learning for Natural Language Processing: Theory and Practice (Tutorial)
- TensorFlow Tutorials
- Practical Neural Networks for NLP from EMNLP 2016 using DyNet framework
- Recurrent Neural Networks with Word Embeddings
- LSTM Networks for Sentiment Analysis
- TensorFlow demo using the Large Movie Review Dataset
- LSTMVis: Visual Analysis for Recurrent Neural Networks
- Using deep learning in natural language processing by Rob Romijnders from PyData Amsterdam 2017
- Richard Socher's talk on sentiment analysis, question answering, and sentence-image embeddings
- Deep Learning, an interactive introduction for NLP-ers
- Deep Natural Language Understanding
- Deep Learning Summer School, Montreal 2016 Includes state-of-art language modeling.
- Tackling the Limits of Deep Learning for NLP by Richard Socher
- Keras - The Python Deep Learning library Emphasis on user friendliness, modularity, easy extensibility, and Pythonic.
- TensorFlow - A cross-platform, general purpose Machine Intelligence library with Python and C++ API.
- PyTorch - PyTorch is a deep learning framework that puts Python first. "Tensors and Dynamic neural networks in Python with strong GPU acceleration."
- SpaCy - A Python package designed for speed, getting things dones, and interoperates with other Deep Learning frameworks
- Genism: Topic modeling for humans - A Python package that includes word2vec and doc2vec implementations.
- fasttext Facebook's library for fast text representation and classification.
- Built on TensorFlow
- SyntaxNet - A toolkit for natural language understanding (NLU).
- textsum - A Sequence-to-Sequence with Attention Model for Text Summarization.
- Skip-Thought Vectors implementation in TensorFlow.
- ActiveQA: Active Question Answering - Using reinforcement learning to train artificial agents for question answering
- BERT - Bidirectional Encoder Representations from Transformers for pre-trained models
- Built on PyTorch
- PyText - A deep-learning based NLP modeling framework by Facebook
- AllenNLP - An open-source NLP research library
- Flair - A very simple framework for state-of-the-art NLP
- fairseq - A Sequence-to-Sequence Toolkit
- fastai - Simplifies training fast and accurate neural nets using modern best practices
- Transformer model - Annotated notebook implementation
- Deeplearning4j’s NLP framework - Java implementation.
- DyNet - The Dynamic Neural Network Toolkit "work well with networks that have dynamic structures that change for every training instance".
- deepnl - A Python library for NLP based on Deep Learning neural network architecture.
- Deep or shallow, NLP is breaking out - General overview of how Deep Learning is impacting NLP.
- Natural Language Processing from Research at Google - Not all Deep Learning (but mostly).
- Context Dependent Recurrent Neural Network Language Model
- Translation Modeling with Bidirectional Recurrent Neural Networks
- Contextual LSTM (CLSTM) models for Large scale NLP tasks
- LSTM Neural Networks for Language Modeling
- Exploring the Limits of Language Modeling
- Conversational Contextual Cues - Models context and participants in conversations.
- Sequence to sequence learning with neural networks
- Efficient Estimation of Word Representations in Vector Space
- Learning Character-level Representations for Part-of-Speech Tagging
- Representation Learning for Text-level Discourse Parsing
- Fast and Robust Neural Network Joint Models for Statistical Machine Translation
- Parsing With Compositional Vector Grammars
- Smart Reply: Automated Response Suggestion for Email
- Neural Architectures for Named Entity Recognition - State-of-the-art performance in NER with bidirectional LSTM with a sequential conditional random layer and transition-based parsing with stack LSTMs.
- Grammar as a Foreign Language - State-of-the-art syntactic constituency parsing using generic sequence-to-sequence approach.
- Natural Language Processing (NLP) progress Tracking the most common NLP tasks, including the datasets and the current state-of-the-art
- A Review of the Recent History of Natural Language Processing
- Deep Learning, NLP, and Representations
- The Unreasonable Effectiveness of Recurrent Neural Networks
- Neural Language Modeling From Scratch
- Machine Learning for Emoji Trends
- Teaching Robots to Feel: Emoji & Deep Learning
- Computational Linguistics and Deep Learning - Opinion piece on how Deep Learning fits into the broader picture of text processing.
- Deep Learning NLP Best Practices
- 7 types of Artificial Neural Networks for Natural Language Processing
- How to solve 90% of NLP problems: a step-by-step guide
- Dataset from "One Billion Word Language Modeling Benchmark" - Almost 1B words, already pre-processed text.
- Stanford Sentiment Treebank - Fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences.
- Chatbot data from Kaggle
- A list of text datasets that are free/public domain in alphabetical order
- Another list of text datasets that are free/public domain in reverse chronological order
- Question Answering datasets
- Quora's Question Pairs Dataset - Identify question pairs that have the same intent.
- CMU's Wikipedia Factoid Question Answers
- DeepMind's Algebra Question Answering
- DeepMind's from CNN & DailyMail Question Answering
- Microsoft's WikiQA Open Domain Question Answering
- Stanford Question Answering Dataset (SQuAD) - covering reading comprehension
Word Embeddings and friends
- The amazing power of word vectors from The Morning Paper blog
- Distributed Representations of Words and Phrases and their Compositionality - The original word2vec paper.
- word2vec Parameter Learning Explained An elucidating explanation of word2vec training
- Word embeddings in 2017: Trends and future directions
- Learning Word Vectors for 157 Languages
- GloVe: Global Vectors for Word Representation - A "count-based"/co-occurrence model to learn word embeddings.
- Dynamic word embeddings for evolving semantic discovery from The Morning Paper blog
- Ali Ghodsi's lecture on word2vec:
- word2vec analogy demo
- TensorFlow Embedding Projector of word vectors
- Skip-Thought Vectors - "unsupervised learning of a generic, distributed sentence encoder"
Have anything in mind that you think is awesome and would fit in this list? Feel free to send me a pull request!
To the extent possible under law, Dr. Brian J. Spiering has waived all copyright and related or neighboring rights to this work.