Natural Language Processing: Bridging the Gap between Human Language and Machine Understanding
Keerthipati Kumar, Mohammad Haider Syed, Saumya Bhargava, Lalit Lalitav Mohakud, Mohammad Serajuddin, Melanie Lourens · 2024
NLP in relation to deep learning models is focused on the review of NLP evolution from the middle of the 1950s up to present days. Today, NLP remains the pillar on which rests our digital age, connecting speech nuances of a human language and the computational power of machines. It is tracked along with the historical evolution from rule based systems to the paradigm shift to the machine learning decade since the nineties and constellations of the NLP components. The methodology involves using a conventional linguistic model alongside ultra-modern machine learning models to overcome ambiguity as well as contexts associated with other languages. The technical specifics and continuity loops under the Human-in-the-loop (HITL) framework are discussed. For example, they look at words like "uncommon" words, which have cultural implications in showing how involved human speech is. For instance, the strength of NPL can be depicted by being used in diverse sectors like health and social media. Finally, this article illustrates an advanced time when smart NPL units give topnotch solutions proving that interconnection between linguistics, computing and artificial intelligence is ongoing.