Recurrent Neural Network-Based Model for Named Entity Recognition with Improved Word Embeddings

Extraction of meaningful information from a huge amount of data available on the web is a quite challenging task. The challenges faced in information extraction can be overcome with the help of an efficient named entity recognition (NER) system. Named entities are the proper names that play an important role in searching important information of interest. In this study, an efficient deep learning-based NER technique has been proposed which recognizes the named entities belonging to the general domain from Hindi, Punjabi, and bilingual Hindi and Punjabi text. An important variant of recurrent neural network, namely bidirectional long short-term memory-based model using improved word embeddings has been developed. Improved word embeddings are the combination of character convolutional neural network embeddings and part of speech embeddings. The main findings of the study include the development of a NER system that can extract named entities not only from Hindi and Punjabi datasets individually but also from mixed Hindi and Punjabi text. Besides, improved word embeddings are the combination of character-level features and word-level features which we find as the novel work as per our knowledge. Improved word embeddings are found to be effective in achieving better results than the results obtained by earlier NER models with deep feature extraction tasks.