Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text

Richa Sharma, Sudha Morwal, Basant Agarwal

Source Title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)15(3)

ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859840|DOI: 10.4018/IJCINI.20210701.oa1

MLA

Sharma, Richa, et al. "Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text." IJCINI vol.15, no.3 2021: pp.1-11. http://doi.org/10.4018/IJCINI.20210701.oa1

APA

Sharma, R., Morwal, S., & Agarwal, B. (2021). Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(3), 1-11. http://doi.org/10.4018/IJCINI.20210701.oa1

Chicago

Sharma, Richa, Sudha Morwal, and Basant Agarwal. "Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.3: 1-11. http://doi.org/10.4018/IJCINI.20210701.oa1

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Abstract

This article presents a neural network-based approach to develop named entity recognition for Hindi text. In this paper, the authors propose a deep learning architecture based on convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) neural network. Skip-gram approach of word2vec model is used in the proposed model to generate word vectors. In this research work, several deep learning models have been developed and evaluated as baseline systems such as recurrent neural network (RNN), long short-term memory (LSTM), Bi-LSTM. Furthermore, these baseline systems are promoted to a proposed model with the integration of CNN and conditional random field (CRF) layers. After a comparative analysis of results, it is verified that the performance of the proposed model (i.e., Bi-LSTM-CNN-CRF) is impressive. The proposed system achieves 61% precision, 56% recall, and 58% F-measure.