Deep Learning for information extraction: Improve your extraction results
This is the second part of a series of articles about Deep Learning methods for Natural Language Processing applications. As mentioned in the previous blog post, we will now go deeper into different strategies of extending the architecture of our system in order to improve our extraction results. This post will elaborate on techniques like word embeddings, residual connections, conditional random fields, as well as character embeddings.
Deep Learning for Information Extraction
This is the first part of a series of articles about Deep Learning methods for Natural Language Processing applications. As a use case I would like to walk you through the different aspects of Named Entity Recognition (NER), an important task of Information Extraction. In this first article I will give you an introduction to this topic. I will describe the baseline Deep Learning architecture for Named Entity Recognition, which is a bidirectional Recurrent Neural Network based on LSTM or GRU.