Better Qualitative Searching for Effecting the Performance of Machine Translation

What is it about?

By improving a better comprehension of the human language for linguistically based human-computer communication, natural language processing (NLP) contributes to the empowerment of intelligent machines. Instead of hand-coding massive sets of rules, NLP can use machine learning to learn these rules automatically by examining a set of rules. With a view to better and clearer results in the endeavor to achieve success in the field of language translation, we have gone through some of the techniques like Machine Translation, Punctuation Restoration, Name Entity Recognition, and Sentiment Analysis. Here we mentioned different state-of-the-art methods for different techniques of NLP. This paper offers an abstract analysis of the current state of language translation techniques.

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Why is it important?

This paper highlights the transformative role of natural language processing (NLP) in enabling seamless human-computer communication, leveraging machine learning to automate rule acquisition. Through an abstract analysis, it delineates state-of-the-art methods across various NLP techniques, enhancing clarity and efficacy in language translation endeavors.

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