A Deep Neural Network based Approach for Entity Extraction in Code-Mixed Indian Social Media Text
Deepak Kumar Gupta, Asif Ekbal, Pushpak Bhattacharyya · 2018
The rise in accessibility of web to the mass has led to a spurt in the use of social media making it convenient and powerful way to express and exchange information in their own language(s).India, being enormously diversified country have more than 168 millions users on social media.This diversity is also reflected in their scripts where a majority of users often switch between their native languages to be more expressive.These linguistic variations make automatic entity extraction both a necessary and a challenging problem.In this paper, we report our work for entity extraction in a code-mixed environment.Our proposed approach is based on the popular deep neural network based Gated Recurrent Unit (GRU) archirecture that automatically discovers the higher level features from the text.We do not make use of any handcrafted features or rules, and therefore our proposed model is quite generic in nature.Our experiments on two benchmark datasets of English-Hindi and English-Tamil language pairs show the F-scores of 66.04% and 53.85%, respectively.