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Semantic Information Extraction from Multi-Corpora Using Deep Learning

Computers, Materials & Continua 2022年第3期70卷 5021-5038页

作者： Sunil Kumar Hanumat G.Sastry Venkatadri Marriboyina Hammam Alshazly sahar Ahmed Idris Madhushi Verma Manjit Kaur School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Amity School of Engineering and Technology Amity UniversityGwalior474003India Faculty of Computers and Information South Valley UniversityQena83523Egypt College of Industrial Engineering King Khalid UniversityAbhaSaudi Arabia Department of Computer Science Engineering Bennett UniversityGreater Noida201310India

Information extraction plays a vital role in natural language processing,to extract named entities and events from unstructured *** to the exponential data growth in the agricultural sector,extracting significant information has become a challenging *** existing deep learningbased 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 *** paper consists of two *** 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 andmultilayer perceptron to find agricultural-based named entity recognition,events,and relations between *** proposed algorithm has been trained and tested on four input corpora i.e.,agriculture,weather,soil,and pest&*** experimental results have been compared with existing techniques and itwas observed that the proposed algorithm outperformsWeighted-SOM,LSTM+RAO,PLR-DBN,KNN,and na飗e Bayes on standard parameters like accuracy,sensitivity,and specificity.

关键词： Agriculture deep learning information extraction weather soil

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