A Deep Neural Framework for Named Entity Recognition with Boosted Word Embeddings.

The upcoming deep neural architectures overpower other previously proposed techniques for named entity recognition by involving fewer efforts but improving accuracy. In the present study, an important variant of recurrent neural network namely, a Bidirectional Long Short-Term Memory which is specially used for sequence classification problems has been used for the NER task. Word contextual features play an important role in the correct prediction of named entities. The present work contributes to developing novel word embeddings i.e. boosted word embeddings which can learn contextual features efficiently. Boosted word embeddings are the combination of character-based convolutional embeddings, part of speech embeddings, and word length embeddings. The results obtained using boosted word embeddings include f-score values of 73.99%, 66.94% and 77.95% respectively for Hindi, Punjabi and bilingual Hindi and Punjabi named entity recognition system. Boosted word embeddings are found effective in raising the accuracy of the named entity recognition model as compared to other models already present in the literature.