Top 10 Natural Language Processing (NLP) Trends in 2021

  • Riya Kumari
  • Oct 16, 2020
  • NLP
Top 10 Natural Language Processing (NLP) Trends in 2021 title banner

Natural Language Processing, often abbreviated as NLP has been a revolution in the use of computers and technical devices ever since. It is a processing unit that converts human commands into computer language and vice versa. This makes the user interface much easier and convenient. Also, this saves the user's time and trouble of going through an entire programming language.


NLP has consistently been extremely applicable in the realm of software engineering since its establishment behind content examination. In turn, it will help normal use-cases, for example, semantic search. Presently, with the interface between individuals and PCs so incredibly reduced, it would appear to be quickness NLP picked up in its initial days isn't going anywhere soon. For example, an amazing invention in NLP is OpenAI’s GPT-3.


As the year advances, trends tend to develop with how the business embraces and uses technology. Thus, read further and you will get to know about the latest trends in NLP for the coming years. So, in this blog, we will discuss several aspects related to NLP like the definition of NLP, its evolution, its business benefits, and mainly we will see what are the latest trends in Natural Language Processing for 2021. Let's start with the basics of NLP.



Discussing the Evolution of NLP


Now, let's talk about the development or evolution of natural language processing. NLP has evolved a lot from its initial form. Most people think that NLP originated when the era of computers arrived. However, this information is not true. 


The origin of NLP can be traced way back to the 1940s. Quite a shock, isn't it? When the soldiers needed to understand messages in another language, NLP was invented. However, it was called Machine Translation (MT) back then. It converted one human language into another. ( for example, Russian to English). Back then, there were not many people who could use MT. This is because the computer language was difficult to learn and only a handful of people knew how to operate such devices.


From here, the idea of NLP originated. NLP was used to transform human language into computer coding and vice-versa. This made the computer easy to use and much more user friendly. (Referred blog: Natural Language Processing: A Discussion for NLP Working and Applications)


Nowadays, NLP is combining itself with AI(Artificial Intelligence) technology. The devices that are using this technology have become very common. From computers and laptops to mobile phones, everyone uses smart assistance for their day to day life tasks. The google assistant is a very known smart assistant. It can set reminders, remember things, make calls, send messages, etc. Another great example of modern-day NLP is Alexa. It is a smart device that can even be used to control lights, TV, and other smart equipment by using voice command.


What is Natural Language Processing (NLP)?


Natural Language Processing or NLP is illustrated as the natural manipulation of normal languages, such as speech and text, by software. It is the one which helps in perfect communication between human language and computer language. If you are still confused then take an example, NLP is the one who makes it possible for computers to read text or hear speech, interpret it, measure sentiment, and select which portions are vital. (Check here, Introduction to NLP for more information)


Accordingly, NLP has a lot more tasks to perform other than analyzing speeches. There are numerous ways to go after processing the human language and these are the symbolic approach, statistical approach, and connectionist approach.


  1. Symbolic approach- The premise behind this methodology is in commonly adopted policies of speech inside a given language which are appeared and recorded by lexical specialists for the computer system to follow.

  2. Statistical approach- This approach to NLP is based on noticeable and recurring illustrations of linguistic manifestations.

  3. Connectionist approach- Now talking about this approach, the connectionist approach to natural language processing is the mixture of both the symbolic approach and the statistical approach. This approach begins with commonly accepted rules of language and tailors them to particular applications from input obtained from statistical inference.


"Perl was designed to work more like a natural language. It's a little more complicated but there are more shortcuts, and once you learned the language, it's more expressive" - Larry Wall


Nowadays, NLP has improved its structure. It is not just about a user interface. It is being used in many smart devices. In the latest AI technologies, NLP plays a vital role. It determines what the user wants to convey to the device. Then, it converts the command into computer language and processes the result into human language and gives an output. With the improvement in technology, the time taken by NLP to respond has been greatly reduced. This has resulted in increased efficiency of NLP. Now, let's move towards the latest trends in natural language processing.


What are the business benefits of NLP?


NLP is helpful for business for many reasons, from client support and discussion analysis to requesting and reviewing systems. If you right now use a chatbot to communicate with clients or partners, NLP is a profoundly important expansion to your arsenal. Also, NLP allows more common discussions, more effective tasks, higher consumer loyalty, deducted costs, and improved analysis. So, let's have a glance at some points that will describe why your chatbot needs NLP.


  • Reduce Costs and Inefficiencies- If you are running a profitable business then you have the power to reduce cost whenever needed. Everyone wishes that their company shall amass a great amount of money. So, NLP-trained chatbots can support you to dramatically decrease expenses related to physical and duplicative duties. (Learn from here, building Telegram chatbot with DialogFlow)

  • Benefit from Market Research and Analysis- NLP chatbots and software systems can accomplish a productive part in market research and analytics. From web-based media comments and client reviews through to inward and outer inquiry queries, planning data and figuring out raw data is an excellent job for an NLP-based chatbot.


"It's like learning a language; you can't speak a language fluently until you find out who you are in that language, and that has as much to do with your body as it does with vocabulary and grammar"- Fred Frith


There are many more benefits like it allocates human resources effectively, enables natural conversations, and improves customer satisfaction.


What are the Top 10 Latest trends in NLP?


Now coming towards the most important part of this blog that is about the latest trends in NLP for the year 2021. In 2020, NLP is achieving popularity quite fast. As we all know that technology is changing day by day so with the development in technology the complexity in NLP is improving. So, here you will find some latest trends in NLP for 2021.


  1. Market Intelligence Monitoring

NLP is expected to make its way in the field of marketing very soon. In the present time, it is only being used in financial marketing. NLP helps in determining the market condition, tender related information etc. In the coming future, it can be a primary key for monitoring the market. Businesses will use intelligence information from NLP to plan their future steps.


  1. Supervised Learning and Unsupervised Learning Collaboration


When supervised learning and unsupervised learning are used together, it gives NLP a next-level potential. For example, people who are working as text analytics need to examine the documents very carefully. The collaboration of supervised and unsupervised learning makes this task easier and less time taking. One helps them to understand all the terms related to the topic while the other one makes the establishment of the relationship between terms easier.


  1. Training NLP Models with Reinforcement Learning


Over the years, Reinforced Learning (RL) has improved a lot. However, even today, training of RL models is very time taking and uncertain. There are fields like training time, sample efficiency, and overall practices in which RL can be improved. This happens when an Rl model is trained from scratch. To solve the issue, data specialists have found a new technique. Nowadays, supervised models are being trained based on NLP and then Reinforced Learning is used to improve or tune them.


  1. Customized Product Recommendations


Nowadays, online shopping has become a trend. Most people prefer it. So, retailers having an online business can use NLP to study the behaviour of customers through the process of recommendation system. This trend is also being used now. Popular shopping apps and websites use the customer's browsing history, buying history, and their wish-list of products to suggest them related products. This makes it easier for the customers to find products as well as the retailers to increase their business.


  1. Accurate Deep learning Classification


The application of Deep Learning in the field of Natural Language Processing can have many sides. This means that it may not be accurate. So, a new technology known as Recurrent Neural Networks (RNN) makes the text classification easier. It simplifies every part of the text. With certain improvements, RNN can soon become a trend that will make the classification of documents much easier, hence there will be an improvement in the field of the analytics platform.


  1. Fine-tuning Models will be Seamless


Models will be made with pre-coded applications for transfer learning. This technology will play a vital role in measuring customer satisfaction. The data will be accurate which will help the industry to progress further based on their customer’s satisfaction level. This application will be beneficial for service industries like medical, media, transportation etc.

This image is showing some latest trends in NLP for 2021.

Some latest trends in NLP for the coming years

  1. Intelligent Semantic Search


The all-new semantic search technology can use NLP to improve searching. This means that the browser will not only search for the individual meaning of words written, rather it will search for the overall meaning of the given term. In this way, the user will find it much easier and less time taking to find any kind of information on the internet.


  1. Sentiment Analysis for Social Media


NLP can be used for sentimental analysis in the future. Social media is a huge influence these days. So, sentimental analysis can be used to analyze the mood, nature, and views of people towards any particular topic. The topic can be related to politics, health, travelling, or any other thing. Analysis of people according to their social media behaviour has proven to be a great way to know about their views and ideas. (More reads: Sentiment Analysis of YouTube comments)


  1. Growth in Chatbots and Virtual Assistants


Chatbots and virtual assistants are very common these days. In the year 2019, the chatbot market was estimated at around 2.6 billion dollars, which is expected to go as far to about 9.4 billion US dollars by the year 2024.


  1. Intelligent Cognitive-Communication


This term is, in a way, similar to semantic search. Cognitive communication means communication which is easily understandable for both parties. This technology will use supervised and unsupervised learning and also other natural language technologies to make any type of communication easy to interpret.




So, after reading the whole blog, we can easily say that Natural Language Processing or NLP is the relationship between human language and computer language. In this blog, we have read about the latest trends in NLP for the year 2021. The new trends of AI will soon be taken over by NLP. 

Also, the trends in the field of NLP itself will gain popularity among developers. Business Intelligence and Consumer behaviour monitoring are achieving recognition. So, we can easily expect that in the coming year natural language processing is going to snatch a tremendous position in the technology market.