Lexical Intent Recognition in Urdu Queries Using Deep Neural Networks

Sana Shams, Muhammad Aslam, Ana María Martínez Enríquez

Abstract: Recognition of user intent from web queries is required by search engines to improve user experience by adapting search results to the user goals. In this paper we report findings of intent recognition from search queries, using two intent annotated benchmark datasets, ATIS and AOL web query dataset. Both these corpora have been automatically translated from English to Urdu. Through multiple experiments, we analyze and compare performance of four Deep Neural Network (DNN) based models and their architectures, i.e. CNN, LSTM, bi-directional LSTM, and CLSTM (CNN+LSTM). On ATIS dataset, CNN achieves 92.4% accuracy on binary classification. While on AOL dataset, BLSTM performs the best with 83.1% accuracy for 5% test sample proportion for 3 intent classes.