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

Autor:	Richa Sharma, Sudha Morwal, Basant Agarwal
Rok vydání:	2021
Předmět:	Hindi business.industry Computer science Deep learning Extraction (chemistry) computer.software_genre language.human_language Human-Computer Interaction Artificial Intelligence language Artificial intelligence business computer Software Natural language processing
Zdroj:	International Journal of Cognitive Informatics and Natural Intelligence. 15:1-11
ISSN:	1557-3966 1557-3958
DOI:	10.4018/ijcini.20210701.oa1
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9e76d757cfc6ff9a91e39b8e4ea675bc https://doi.org/10.4018/ijcini.20210701.oa1 Zobrazit plný text záznamu