Retrieval of meaningful information out of voluminous data available on the internet is a big challenge these days. Named entity recognition system deals with this challenge efficiently and achieves promising results in accessing information of interest in several NLP applications. The present study proposes a deep learning-based named entity recognition system using hybrid embedding which is the combination of fasttext and bidirectional LSTM based character embedding. These embeddings capture the contextual, syntactic and semantic properties of the text which improves the cognitive power of deep learning methods. We have performed different experiments with important variants of recurrent neural network (RNN) namely long short-term memory network (LSTM) and bidirectional LSTM as well as gated recurrent unit (GRU) and bidirectional GRU on manually annotated Punjabi dataset and annotated Hindi dataset collected from international joint conference on natural language processing (IJCNLP-08) website. We have also explored different word embeddings and character embeddings for named entity recognition task. Out of all the experiments, the bidirectional GRU model using hybrid embedding has outperformed with precision, recall, and f-score values of 84%, 83%, and 83.50% respectively for Punjabi named entity recognition and 75%, 77%, 75.99% respectively for Hindi named entity recognition.

Increase in Chest X-ray (CXR) imaging tests has burdened radiologists, thereby posing significant challenges in writing radiological reports on time. Although several deep learning-based automatic report generation methods have been developed, most are over-parameterized. For deployment on edge devices with constrained processing power or limited resources, over-parameterized models are often too large. This article presents a compressed deep learning-based model that is 30% space efficient compared to the non-compressed base model, while both have comparable performance. The model comprising VGG19 and hierarchical long short-term memory equipped with a contextual word embedding layer is used as the base model. The redundant weight parameters are removed from the base model using unstructured one-shot pruning. To overcome the performance degradation, the lightweight pruned model is fine-tuned over publicly available OpenI dataset. The quantitative evaluation metric scores demonstrate that proposed model surpasses the performance of state-of-the-art models. Additionally, the proposed model, being 30% space efficient, is easily deployable in resource-limited settings. Thus, this study serves as baseline for development of compressed models to generate radiological reports from CXR images.