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Session 1 - SENNA
Session 2 - Sentence CNN
Session 3 - Relation CNN
Session 4 - LSTM Sequence Classification
1_Theory_Introduction.pdf
1_Theory_Introduction.pptx
2_Theory_Frameworks.pdf
2_Theory_Frameworks.pptx
3_Theory_Convolutional_NN.pdf
3_Theory_Convolutional_NN.pptx
4_Theory_Recurrent_Neural_Networks.pdf
4_Theory_Recurrent_Neural_Networks.pptx
README.md

README.md

Deep Learning for NLP - Seminar November 2016

This GIT repository accompanies the UKP seminar on Deep Learning for Natural Language Processing held at the University of Duisburg-Essen.

In contrast to other seminars, this seminar focuses on the usage of deep learning methods. As programming infrastructure we use Python in combination with Theano and Keras. The published code uses Python 2.7, Theano 0.8.2 and Keras 1.1.1. You should ensure that you have the frameworks installed in the right version (note: they change quickly).

This seminar is structured into 4 sessions:

  1. Feed-Forward Networks for Sequence Classification (e.g. POS, NER, Chunking)
  2. Convolutional Neural Network for Sentence / Text Classification (e.g. sentiment classification)
  3. Convolutional Neural Network for Relation Extraction (e.g. semantic relation extration)
  4. Long-Short-Term-Memory (LSTM)-Networks for Sequence Classificaiton

The seminar is inspired by an engineering mindset: The beautiful math and complexity of the topic is sometimes neglected to provide instead an easy-to-understand and easy-to-use approach to use Deep Learning for NLP tasks (we use what works without providing a full background on every aspect).

At the end of the seminar you should be able to understand the most important aspect of deep learning for NLP and be able to programm and train your own deep neural networks.

In case of questions, feel free to approach Nils Reimers.

Recommended Readings on Deep Learning

The following is a short list with good introductions to different aspects of deep learning.

Theory 1 - Introduction to Deep Learning

Slides: pdf

The first theory lesson covers the fundamentals of deep learning.

Theory 2 - Introduction to Deep Learning Frameworks

Slides: pdf

The second lesson gives an overview of deep learning frameworks. Hint: Use Keras and have a look at Theano and TensorFlow.

Code Session 1 - SENNA Architecture for Sequence Classification

Slides: pdf

Code: See folder Session 1 - SENNA

The first code session is about the SENNA architecture (Collobert et al., 2011, NLP (almost) from scratch). In the folder you can find Python code for the preprocessing as well as Keras code to train and evaluate a deep learning model. The folder contains an example for Part-of-Speech tagging, which require the English word embeddings from Levy et al..

You can find in this folder also an example for German NER, based on the GermEval 2014 dataset. To run the German NER code, you need the word embeddings for German from our website.

Recommended Readings:

Theory 3 - Introduction to Convolutional Neural Networks

Slides: pdf

This is an introduction to Convolutional Neural Networks.

Recommended Readings:

Code Session 2 - Convolutional Neural Networks for Text Classification

Slides: pdf

Code: See folder Session 2 - Sentence CNN

This is a Keras implementation of the Kim, 2014, Convolutional Neural Networks for Sentence Classification. We use the same preprocessing as provided by Kim in his github repository but then implement the rest using Keras.

Code Session 3 - Convolutional Neural Networks for Relation Extraction

Slides: pdf

Code: See folder Session 3 - Relation CNN

This is an implementation for relation extraction. We use the SemEval 2010 - Task 8 dataset on semantic relations. We model the task as a pairwise classification task.

Recommended Readings:

Theory 4 - Introduction to LSTM

Slides: pdf

Code: See folder Session 4 - LSTM Sequence Classification

LSTMs are a powerful model and became very popular in 2015 / 2016.

Recommended Readings:

Code Session 4 - LSTM for Sequence Classification

Slides: pdf

The folder contains a Keras implementation to perfrom sequence classification using LSTM. We use the GermEval 2014 dataset for German NER. But you can adapt the code easily to any other sequence classification problem (POS, NER, Chunking etc.). Check the slides for more information.