IIT(Bhu)@Iecsil-Fire-2018: Language independent automatic framework for entity extraction in Indian languages

Abstract

The paper discusses about our work submitted to the track IECSIL [3] organized by ARNEKT in conjunction with Forum for Information Retrieval and Evaluation 2018. The track primarily focuses on developing language independent system for information extraction on Indian languages. We focused on the identification and categorization of entities in the text. We used word embedding for the feature representation. We proposed Bidirectional LSTM recurrent neural network model for entity extraction from the provided text for the five Indian languages such as Hindi, Kannada, Malayalam, Tamil and Telugu. The proposed technique is evaluated in terms of two metrics, accuracy and F1-score. © 2018 CEUR-WS. All Rights Reserved.