With the success of ChatGPT, more and more companies the world over are availing the use of Natural Language Processing Trends interaction for their products. AI-powered platforms have many Natural Language Processing innovations available.

Natural Language Processing Trends
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NLP Trends processes human natural language using AI to make it compatible with the computer language. The computer collects written human language using programs and spoken language through microphones to register the audio. Two main phases involved in this task are data preprocessing and algorithm development. Three tools commonly used in Natural Language Processing are Natural Language Toolkit, Gensim, and Intel Natural Language Processing Architect.


 Some common applications of Natural Language Processing Trends can be Google translate, automated litigation analysts, tools like Duplichecker created for checking plagiarism, Grammarly for correcting grammar and sentence formation, Semantic Scholar, and Perplexity for in-depth academic research and analysis, Genei for academic writing, technical analysis for stocks, AI-based medical record analysis, etc.

Top 7 Natural Language Processing Trends

Natural Language Processing Trends
Pic courtesy Tatiana Shepeleva/Shutterstock.com

1. Virtual Assistants

AI based virtual assistants are like spokespersons who work as a voice-based interface between the company and the clients or end users. They interact with the users through natural language interaction software. On the one hand, these apps help in planning and implementing for example accessing information, booking holidays or travel arrangements, scheduling meetings, answering emails or queries, and interpreting data, and on the other hand, can control the Smart appliances and guide in navigation. VI is one of the most popular of the present NLP Trends with Microsoft’s Cortana, Apple’s Siri, Amazon’s Alexa, Google Assistant, and Youper an emotional health assistant app to name a few

2. Sentiment Analysis  

Also known as opinion mining, Sentimental Analysis is another popular approach to NLP Trends. This trend involves ascertaining, classifying, and attributing positive, negative, or neutral variables to the data of opinion collected. The statistics obtained may be reviews for a product, a movie, a service, market research, or an idea. Customer support, surveys, emails, web chats, and social media monitoring tools help gather feedback and consumer opinions. AI-based platforms help to judge the emotional tone behind the feedback. This is useful for new products or services launched, dipped sales of existing products or services, or for implementing new ideas. Sentimental Analysis uses a combination of data mining, deep learning, and language tools. Brandwatch, Lexalytics, Critical Mention platforms, and the BERT model are some of its APIs.

3. Language Transformers

As the name suggests, they transform one sequence into another. In these semi-supervised learning models, large data is inputted in a pre-trained unsupervised sequence, processed in a supervised environment, and then output delivered. It is a Deep Learning model. Language transformers as Natural Language Processing Trends are very powerful tools. These NLP Trends can not only translate text to speech and vice-versa from one language to another and video understanding but also produce documents, articles, poems, and play chess! Large language models like GPT3, and ChatGPT can perform thanks to Transformers.GPT is said to be one of the most powerful NLP language models so far. GPT4 is now under process.

4. Multilingual Language Models

This is an NLP Trends model that enables programs to process and understand large amounts of text in human language; analyze it and produce it in the language required and also provide key insights. Organizations with branches in different countries may need to extract information that may be in a foreign language. Multilingual Large Language Model (LLM) can make this process smooth and quick without the danger of information leaks. Bloom is a popular example of Multilingual LLM.

5. Semantic Language Search

Semantic search is a data searching technique wherein the search engine tries to understand the intent of the query and then processes the information to provide the solution or results closest as applicable to the query. It uses techniques like Text mining, Natural Language Processing Trends, Sentiment Analysis, etc. for processing information and queries. Companies would be able to respond quickly to customer queries and resolve issues faster.

6. Transfer learning

This machine-learning technique uses knowledge acquired by the computer program from one task to apply to another task. Visual data or textual information, etc is stored in the computer program during one task. This data is later on used to solve issues in some other task elsewhere. For example, machine learning models can be used in different fields with the help of transfer learning. In the case of litigations, the information regarding the rulings given in one case can be used in another case of automation of litigation tasks. Knowledge of one language can be used to detect different dialects or elements of that language.

7. Named Entity recognition

This form of NLP Trends involves detecting and classifying textual information into different pre-defined categories inputted like names, locations, entities, money, and others. NER tasks use dictionary and rules data embedded in NLP along with algorithms in machine learning. Deep learning NER is quite an accurate method for this task. This is multitasking while deciphering topic-specific words and performing semantic and syntactic tasks. SpaCy is an open-source library for Natural Language Processing Trends and has inbuilt techniques for Name Entity Recognition.


A hybrid of the NLP Trends which is a combination of Artificial Intelligence and Machine Language called Hybrid AI is a popular medium for programming. IBM’s Global AI Adoption Index in 2022 estimated that around 35% of companies worldwide are using artificial intelligence, while another 42% exploring the prospects of integrating NLP trends in their operations. However, companies still face difficulties in getting qualified or trained personnel to implement these Natural Language Processing Trends. Many offline and online institutes offer courses and train students in this field. Henry Harvin is one of the leading online institutes which offers more than 800 courses in different fields.

1. Does Natural Language Processing Trends have a huge Global market?

A. From around $20 billion in 2022 to $ 24 billion in 2024, the global market of NLP  is estimated to grow to around $112 billion within 8 years.

2. Is Natural Language Processing a BI trend in 2024?

A. NLP trends are gaining popularity due to their fast processing and multitasking. Also, the recent success of Chatbots has contributed to its popularity in Business Intelligence.

3. What skills are required to become a NLP Engineer?

A. Apart from the required qualifications, knowledge of Python, Java, and R for programming and experience with machine language to write robust codes.

4. Where can I study Natural Language Processing?

A. There are many offline and Online institutes and colleges where you can enroll. Henry Harvin’s Natural Language Processing Course and Postgraduate program in AI and Machine Language are among the many courses offered in this field.

5. What is the salary of a Natural Language Processing Trends Engineer?

A. The average salary of a NLP Engineer in India is Rs.9.5 Lakhs per annum and the range is from Rs 1 Lakhs to Rs 18 Lakhs per year which can go up depending on the qualification and experience.

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