April 7, 2019

Introduction to n-gram language models

You might have heard, that neural language models power a lot of the recent advances in natural language processing. Namely large models like Bert and GPT-2. But there is a fairly old approach to language modeling that is quite successful in a way. Read more

July 1, 2018

State-of-the-art named entity recognition with residual LSTM and ELMo

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.

June 2, 2018

Debugging black-box text classifiers with LIME

Often in text classification, we use so called black-box classifiers. By black-box classifiers I mean a classification system where the internal workings are completely hidden from you. A famous example are deep neural nets, in text classification often recurrent or convolutional neural nets. Read more

June 2, 2018

Explain neural networks with keras and eli5

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.

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