How to build deep neural network for custom NER with Keras

Image courtesy : Google

Introduction

In this post, we will learn how we can create a simple neural network to extract information ( NER) from unstructured text data with Keras.

Named Entity Recognition (NER)

NER is also known as entity identification or entity extraction. It is a process of identifying predefined entities present in a text such as person name, organisation, location, etc. It is a statistical model which is trained on a labelled data set and then used for extracting information from a given set of data.

Sometimes we want to extract the information based on our domain or industry. For example : in medical domain, we want to extract disease or symptom or medication etc, in that case we need to create our own custom NER.

Model Architecture

Here we will use BILSTM + CRF layers. The LSTM layer is used to filter the unwanted information and will keep only the important features/information and the CRF layer is used to deal with the sequential data.

BI-LSTM Layer

BI-LSTM is used to produce vector representation for our words. It takes each word in a sentence as an input and produce a vector representation of each word in both…