In today's digital world, we are constantly generating vast amount of text data through emails, social media posts, articles, and more. Understanding and making sense of this unstructured text data is a challenging task. So now, Natural Language Processing (NLP) comes to play an important role here. In this blog post, we'll explore what is NLP, its applications, which technologies it uses, and the future of NLP market.
What is Natural Language Processing (NLP)?
Natural Language Processing, often abbreviated as NLP, is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. Its goal is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. NLP combines techniques from linguistics, computer science, and machine learning to process, analyse, and extract insights from text and speech data. The NLP controls computer programs that translate text from one language to another, respond to voice commands and quickly summarize large volumes of text, even in real-time.
The NLP market is a rapidly growing industry focused on developing technology to enable computers to understand, interpret and generate human language. According to our latest report projections, the natural language processing market is set to surge from a valuation of $29.1 billion in 2023 to an impressive $92.7 billion by 2028, demonstrating a compound annual growth rate (CAGR) of 26.1% during this forecast period 2023 to 2028.
Our report predicts North America contributes 36.1%, in terms of value, followed by Asia-Pacific and Europe for NLP market.
The Role of Artificial Intelligence (AI) in NLP
Artificial Intelligence (AI) plays a crucial role in Natural Language Processing (NLP), which is a subfield of AI focused on enabling computers to understand, interpret, and generate human language.
Below are some major points of AI in NLP:
- Language Understanding: AI-powered NLP models can analyse and understand human language, including its syntax, semantics, and context.
- Text Classification: AI models are used to categorize and classify text data into predefined categories or labels.
- Chatbots and Virtual Assistants: AI-driven chatbots and virtual assistants use NLP to understand and respond to user queries and commands in a conversational manner. These systems are deployed in customer support, information retrieval, and automation.
- Information Retrieval: NLP techniques are applied to search engines to help users find relevant information more effectively.
- Speech Recognition: Speech-to-text technology, powered by AI, converts spoken language into text.
- Language Translation Services: AI-driven translation services like Google Translate use deep learning techniques to provide accurate translations between hundreds of languages.
