A comprehensive review of Semantic Information Extraction using Deep Learning Technique for Agricultural domain

Abstract

Information Extraction (IE) from unstructured corpus become an ever-green problem 2 due to the advancements in electronic data collection tools, exceptional growth of online and offline 3 publication data etc. Named Entity Extraction (NER) and Event Extraction (EE) are most popular Information Extraction tasks among various IE tasks. It becomes more essential to find out precise analytical methods, capable of processing and analyzing large amounts of data with reliable information and accurate predictions. Deep Learning Technique (DLT)is an analytical approach to play vital role for dealing real-time analysis on unstructured corpus especially for agricultural domain. This paper reviews recent studies to solve a variety of agricultural problems based on agricultural data using the practices of DLT. Further, we also propose a framework for Semantic Information Extraction using Machine Learning Technique (MLT) with input values as agricultural corpus.