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Machine Translation and Natural Language Processing

International Journal of Science and Management Studies (IJSMS)
© 2024 by IJSMS Journal
Volume-7 Issue-1
Year of Publication : 2024
Authors : Chinedu Chukwuemeka Mazi, Ogechi Ifeoma Anya, Andrew Chinonso Nwanakwaugwu, Gregory Anichebe
DOI: 10.51386/25815946/ijsms-v7i1p110
Citation:
MLA Style: Chinedu Chukwuemeka Mazi, Ogechi Ifeoma Anya, Andrew Chinonso Nwanakwaugwu, Gregory Anichebe "Machine Translation and Natural Language Processing" International Journal of Science and Management Studies (IJSMS) V7.I1 (2024): 65-69.

APA Style: Chinedu Chukwuemeka Mazi, Ogechi Ifeoma Anya, Andrew Chinonso Nwanakwaugwu, Gregory Anichebe, Machine Translation and Natural Language Processing, International Journal of Science and Management Studies (IJSMS), v7(i1), 65-69.
Abstract:
Machine translation and natural language processing are powerful tools that allow for the automated translation of text from one language to another. Machine translation systems have evolved from rule-based systems to statistical and neural network-based approaches, with each approach having its own strengths and weaknesses. NLP, on the other hand, focuses on understanding the structure and meaning of natural language and includes tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. Thegoalofmachinetranslation,a subfield of natural language processing (NLP), is to create platform that translate text or speechautomatically.RBMT provides linguistic accuracy and control, but linguistic resources must be built and maintained meticulously. However, SMT makes use of large-scale parallel corpora to automatically learn translation patterns, making it adaptable to new language pairs and context-dependent translations.However, SMT may struggle with handling grammatical nuances and domain-specific vocabulary. Hybrid approaches combining the best of both RBMT andSMT and newer approaches like neural machine translation (NMT) are being explored to overcome the limitations and improve the quality of translation output.
Keywords: Machine translation, Natural language processing, Rule-based systems.
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