Natural Language Processing: State of The Art, Current Trends and Challenges

TL;DR

This paper reviews the evolution, current state, and challenges of NLP, highlighting its diverse applications and the progression through different levels and components of natural language understanding and generation.

Contribution

It provides a comprehensive overview of NLP's history, current trends, and challenges, emphasizing the various applications and technological advancements in the field.

Findings

NLP has expanded into numerous applications like translation and summarization.

The paper identifies key challenges in NLP development and deployment.

It discusses the evolution of NLP components and levels over time.

Abstract

Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.