Natural language processing: state of the art, current trends and challenges

Area of Science:

• Computer Science
• Artificial Intelligence
• Computational Linguistics

Background:

• Natural Language Processing (NLP) is crucial for computational analysis of human language.
• NLP has diverse applications including machine translation, spam detection, and medical informatics.

Purpose of the Study:

• To provide a comprehensive overview of Natural Language Processing.
• To discuss the evolution, state-of-the-art, applications, trends, and challenges in NLP.

Main Methods:

• Distinguishing four phases of NLP and Natural Language Generation (NLG) components.
• Reviewing the history and evolution of NLP.
• Detailing current applications, trends, challenges, datasets, models, and evaluation metrics.

Main Results:

• NLP encompasses distinct phases and NLG components.
• The field has evolved significantly with widespread applications.
• Current trends and challenges highlight areas for future research.

Conclusions:

• NLP is a rapidly advancing field with broad impact.
• Understanding NLP's history, components, and applications is essential.
• Further research is needed to address existing challenges and leverage new datasets and models.