An Anatomization of Language Detection and Translation using NLP Techniques
Bhagyashree P Pujeri1, Jagadeesh Sai D2
1Bhagyashree P Pujeri, Information Science, RIT, Bangalore, India.
2Jagadeesh Sai D, Information Science, RIT, Bangalore, India.
Manuscript received on November 17, 2020. | Revised Manuscript received on November 22, 2020. | Manuscript published on December 10, 2021. | PP: 69-77 | Volume-10 Issue-2, December 2020 | Retrieval Number: 100.1/ijitee.B82651210220| DOI: 10.35940/ijitee.B8265.1210220
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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 issue with identifying language relates to process of determining natural language in which specific text is written. This is one of the big difficulties in the processing of natural languages. Still, they also pose a problem in improving multiclass classification in this area. Language detection and translation a significant Language Identification task are required. The language analysis method may be carried out according to tools available in a particular language if the source language is known. A successful language detection algorithm determines the achievement of the sentiment analysis task and other identification tasks. Processing natural language and machine learning techniques involve knowledge that is annotated with its language. Algorithms for natural language processing must be updated according to language’s grammar. This paper proposes a secure language detection and translation technique to solve the security in natural language processing problems. Language detection algorithm based on char n-gram based statistical detector and translation Yandex API is used. While translating, there should be encryption and decryption for that we are using AES Algorithm.
Keywords: Language Identification, Natural Language Processing (NLP), AES, N-gram, Language Detection, and Translation.
Scope of the Article: Natural Language Processing