Open Access

ARTICLE

Semantic Information Extraction from Multi-Corpora Using Deep Learning

by Sunil Kumar¹, Hanumat G. Sastry¹, Venkatadri Marriboyina², Hammam Alshazly^3,*, Sahar Ahmed Idris⁴, Madhushi Verma⁵, Manjit Kaur⁵

1 School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248001, India
2 Amity School of Engineering and Technology, Amity University, Gwalior, 474003, India
3 Faculty of Computers and Information, South Valley University, Qena, 83523, Egypt
4 College of Industrial Engineering, King Khalid University, Abha, Saudi Arabia
5 Department of Computer Science Engineering, Bennett University, Greater Noida, 201310, India

* Corresponding Author: Hammam Alshazly. Email: .e.g.

(This article belongs to the Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)

Computers, Materials & Continua 2022, 70(3), 5021-5038. https://doi.org/10.32604/cmc.2022.021149

Received 25 June 2021; Accepted 30 July 2021; Issue published 11 October 2021

Abstract

Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity in input corpora, and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer and multilayer perceptron to find agricultural-based named entity recognition, events, and relations between them. The proposed algorithm has been trained and tested on four input corpora i.e., agriculture, weather, soil, and pest & fertilizers. The experimental results have been compared with existing techniques and it was observed that the proposed algorithm outperforms Weighted-SOM, LSTM+RAO, PLR-DBN, KNN, and Naïve Bayes on standard parameters like accuracy, sensitivity, and specificity.

---

Keywords

---

Citations