Modelling Simple and Efficient Data Transformation Scheme for Improving Natural Language Processing
MShruthi J1, Suma Swamy2
1Shruthi J*, Assistant Professor, Department of Computer Science & Engineering, BMSITM, Bengaluru, India.
2Suma Swamy, Professor, Departement of Computer Science &Engineering, Sir MVIT, Bengaluru, India
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1479-1485 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8185019320/2020©BEIESP | DOI: 10.35940/ijitee.C8185.019320
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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: The importance of natural language processing cannot be sided in the current era of communication and analytics where data is exponentially growing. Although, there has been various versions and schemes that has evolved in the past decade towards improving the performance of natural language processing, but still the problem towards precisely extracting the actual context is an open ended. Review of existing studies show a large scope of new work towards improving it. Therefore, this manuscript presents a unique simplified approach where natural language processing is carried out with a combined effort of syntactical and semantic based transformation scheme. The study is implemented using analytical methodology while the secondary motive of the work is also to balance the mining performance as well as optimizing storage performance too. The study outcome shows proposed scheme to excel better performance with respect to time duration in all the internal processes being involved for data transformation.
Keywords: Natural Language Processing, Text Mining, Analytics, Context, Cloud
Scope of the Article: Natural Language Processing