Bidirectional LSTM joint model for intent classification and named entity recognition in natural language understanding
Akson Sam Varghese, Saleha Sarang, Vipul Yadav, Bharat Karotra, Niketa Gandhi · International Journal of Hybrid Intelligent Systems · 2019
Recurrent Neural Networks (RNN) have claimed to achieve the state of the arts results in some cases, better performances than humans could have, especially RNN – Long Short Term Memory (LSTM) and RNN – Bidirectional LSTM, Attention based LSTM encoder-decoder networks in the domains of Speech Recogn ition, Sequence Labeling, Text Classification, Image Caption Generation and many more. The main focus of this paper is to present here a simple LSTM – Bidirectional LSTM joint model for Intent Classification and Named Entity Recognition (NER) with and without Convolutional Neural Network (CNN) as feature extractor. The aim of this experiment is to improve the accuracy of the model through inducing information from a well-performing model on a particular task to another model in a joint model framework and conclude if is there any correlation that might aid in syntactic and semantic structural learning of the task through the application of learned weights. The comparative results of models with and without CNN as feature extractors prepended to the models are tabulated as well.