This is the second post of my series about understanding text datasets. If you read my blog regularly, you probably noticed quite some posts about named entity recognition. In this posts, we focused on finding the named entities and explored different techniques to do this. Read more

This is the first post of my series about understanding text datasets. A lot of the current NLP progress is made in predictive performance. But in practice, you often want and need to know, what is going on in your dataset. Read more

Once named entities have been identified in a text, we then want to extract the relations that exist between them. As indicated earlier, we will typically be looking for relations between specified types of named entity. Read more

An important part of every machine learning project is the proper evaluation of the performance of the system. In this post we will talk about evaluation of token-based sequence models. This is especially tricky because: Read more

In the last post, I introduced the U-Net model for segmenting salt depots in seismic images. This time, we will see how to improve the model by data augmentation and especially test time augmentation (TTA). You will learn how to use data augmentation with segmentation masks and what test time augmentation is and how to use it in keras. Read more

Today I’m going to write about a kaggle competition I started working on recently. In the TGS Salt Identification Challenge, you are asked to segment salt deposits beneath the Earth’s surface. So we are given a set of seismic images that are $101 \times 101$ pixels each and each pixel is classified as either salt or sediment. Read more

This is the sixth post in my series about named entity recognition. This time I’m going to show you some cutting edge stuff. We will use a residual LSTM network together with ELMo embeddings, developed at Allen NLP. You will learn how to wrap a tensorflow hub pre-trained model to work with keras. The resulting model with give you state-of-the-art performance on the named entity recognition task.

In this post, I’m going to show you how you can use a neural network from keras with the LIME algorithm implemented in the eli5 TextExplainer class. For this we will write a scikit-learn compatible wrapper for a keras bidirectional LSTM model. The wrapper will also handle the tokenization and the storage of the vocabulary.

Last weekend I participated at the PyData Amsterdam 2018 Conference in, you guess it, in Amsterdam. It has been a great conference and I meet a lot of great people and had a very good time in Amsterdam. Read more