Natural Language Processing- An intermediator between human and machines

This project looks at how Natural Language Processing (NLP) has grown into a key part of human‑computer interaction (HCI). It starts with early rule‑based machine translation, such as the 1954 Georgetown‑IBM experiment, and ends with today’s deep‑learning models. The main issue is building strong, easy‑to‑use interfaces that let people talk to computers, no matter their skill level or physical abilities. The paper points out that moving to neural‑network architectures in the 2010s made it possible to process longer sequences of text. That opened up tasks like document classification, spam filtering, and sentiment analysis for customer feedback, support tickets, and social media posts. For M.Tech students, this summary gives a base

for comparing classic statistical methods with modern deep‑learning sequence models. Since the original paper is mostly a historical and conceptual review, it does not provide new datasets or a new algorithm. Therefore, the implementation help will concentrate on choosing common benchmark datasets and setting up solid evaluation methods to test model performance on sentiment extraction and text classification.

Message: Hi FE, I need help with “Natural Language Processing- An intermediator between human and machines” in “Computer Science & Engineering”