Improving Named Entity Recognition using Deep Learning with Human in the Loop

Named Entity Recognition (NER) is a challenging problem in Natural Language Processing (NLP). Deep Learning techniques have been extensively applied in NER tasks because they require little feature engineering and are free from language-specific resources, learning important features from word or character embeddings trained on large amounts of data. However, these techniques are data-hungry and require a massive amount of training data. This work proposes Human NERD (stands for Human Named Entity Recognition with Deep learning) which addresses this problem by including humans in the loop. Human NERD is an interactive framework to assist the user in NER classification tasks from creating a massive dataset to building/maintaining a deep learning NER model. Human NERD framework allows the rapid verification of automatic named entity recognition and the correction of errors. It takes into account user corrections, and the deep learning model learns and builds upon these actions. The interface allows for rapid correction using drag and drop user actions. We present various demonstration scenarios using a real world data set.