awesome-dl4nlp

A complete survey of the field with videos, lecture slides, and sample student projects

Video playlist

Previous course notes

Probably the best "book" on DL for NLP

Course code

Pytorch implementations of various Deep NLP models in cs-224n

by Patrick Harrison and Matthew Honnibal

by Yoav Goldberg and Graeme Hirst

by Li Deng and Yang Liu

by Hobson Lane, Cole Howard, and Hannes Hapke

by Taweh Beysolow II

by Douwe Osinga

by Palash Goyal, Sumit Pandey, Karan Jain

by Charu C. Aggarwal

by Thushan Ganegedara

by Hobson Lane and Maria Dyshel

by Ekaterina Kochmar

by Stephan Raaijmakers

Practical Neural Networks for NLP

from EMNLP 2016 using DyNet framework

Includes state-of-art language modeling

General overview of how Deep Learning is impacting NLP

Not all Deep Learning (but mostly)

Models context and participants in conversations

State-of-the-art performance in NER with bidirectional LSTM with a sequential conditional random layer and transition-based parsing with stack LSTMs

State-of-the-art syntactic constituency parsing using generic sequence-to-sequence approach

Tracking the most common NLP tasks, including the datasets and the current state-of-the-art

Opinion piece on how Deep Learning fits into the broader picture of text processing

Almost 1B words, already pre-processed text

Fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences

from The Morning Paper blog

The original word2vec paper

An elucidating explanation of word2vec training

A "count-based"/co-occurrence model to learn word embeddings

from The Morning Paper blog