Location Identification Using Stanford NLP

Abstract

—Even since the 40’s the scope of natural language processing has been primal dismay in computer science and Artificial Intelligence. It aspires to include the next strive forward in Artificial Knowledge which can perform both computers and Individual work with better malleability and apprehension. It incorporates various methods like machine translation, speech recognition, online learning, auto tutor etc. Researchers recalled it as a potential bridge that can amalgamate human spoken language and computer which uses programming language and binary codes. Since it is an impossible task to prepare a computer to recognize human natural language, further techniques and enhancements will foster the demanding yet rewarding and innovative computational trends. This paper confers a restrained domain metaphysical model for agriculture cultivation question answering system. The question answering system has been noted as a significant tactic for knowledge engineering research. Ontologies facilitate the computers in deciphering the restrain domain concept of semantics. Thus forming a significant technique for the question-answering system. This paper inculcates introduction ontology and the definition of a domain ontology for agriculture cultivation. The paper also focused on presenting the restricted domain ontology models and concept level vector space model of information retrieval.