Methods and Trends in Natural Language Processing Applications in Big Data
Joseph M. De Guia1, Madhavi Devaraj2
1Joseph M. De Guia, School of Information Technology, Mapua University, Muralla St., Intramuros, Manila Philippines.
2Madhavi Devaraj, School of Information Technology, Mapua University, Muralla St., Intramuros, Manila Philippines.
Manuscript received on 03 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript Published on 23 May 2019 | PP: 234-249 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F10390476S519/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Understanding the natural language of humans by processing them in the computer makes sense for applications in solving practical problems. These applications are present in our systems that automates some and even most of the human tasks performed by the computer algorithms. The “big data” deals with NLP techniques and applications that sifts through each word, phrase, sentence, paragraphs, symbols, images, speech, utterances with its meanings, relationships, and translations that can be processed and accessible to computer applications by humans. Through these automated tasks, NLPs achieved the goal of analyzing, understanding, and generating the natural language of human using a computer application and with the help of classic and advanced machine learning techniques. This paper is a survey of the published literature in NLP and its uses to big data. This also presents a review of the NLP applications and learning techniques used in some of the classic and state-of-the art implementations.
Keywords: Natural Language Processing, Deep Learning, Artificial Neural Networks, Big Data Applications.
Scope of the Article: Big Data Analytics