How do we proceed if we have almost no labeled data for a machine learning model? One answer may be: combining all the knowledge we have (labeled data, distant superv
In this blogpost, I want to present my master's thesis, which focused on transfer learning for text classification using Siamese Networks.
Text classification is a field in natural language processing (NLP), which assigns text to given classes. With applications in sentiment analysis, spam detection or i
We extend state-of-the-art sequence-to-sequence neural networks for summarization of long text across windows. By learning transitions, we are able to process arbitrarily long texts during inference.
This blog post describes my master thesis "Abstractive Summarization for Long Texts". We’ve extended existing state-of-the-art sequence-to-sequence (Seq2Seq) neural net
I use state-of-the-art NLP techniques to improve an existing pricing model in an online car market. Online car markets usually use technical car attributes for price prediction with sellers adding description texts to provide more details. In my thesis, I use these texts to improve the existing pricing model.
tl;dr: This blog post summarizes my masters‘ thesis. I use state-of-the-art NLP techniques to improve an existing pricing model in an online car market. Online
This article presents SeqPolicyNet, our Deep Learning approach to accessing information stored in an Elasticsearch instance given natural language questions.
tl;dr (spoiler alert): We’ve trained an advanced neural network to query Elasticsearch