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NLP Trends in 2021: Top Natural Language Processing technologies to be used in 2021 for Artificial Intelligence and Machine Learning applications

New Delhi, Mon, 30 Nov 2020 Deepak Kumar

During 2020 we have seen tremendous growth in the use of Natural Language Processing (NLP) technologies for developing for AI applications. We have seen the release of  GPAT-3, Microsoft Research’s Turing-NLG and other in 2020. Now in 2021 we are expecting big use of NLP in the development of various AI applications that will enable the Data Scientists to develop many advanced applications.

The high growth of NLP is due to availability of store and data processing capabilities. Most of the companies are using cloud based computing to process their vast amount of data. So, in 2021 we will see the upward trend in the use of cloud computing.

We have researched and presenting here the top NLP trends that will be in high demand in 2021. Data Scientists should learn these technologies for the better prospects in 2021.

  1. Cognitive-Communication

Cognitive-Communications are techniques and techniques that help people to function successfully and understand another person meaningfully. For instance, some people are born with a certain type of intelligence that they use to understand others. If that ability continues to develop over time, it results in more use of the more specialized cognitive-communications techniques. The Cognitive-Communications includes many processes such as like orientation, attention, memory, problem solving, and executive function, these enables the person to communicate in a better way. The use of NLP, machine learning and artificial intelligence will help in cognitive computing. There will be heavy uses of NLP techniques in 2021 for effective Cognitive-Communications. Developer should learn NLP and Cognitive computing for better job prospects in 2021.

  1. Semantic Search

The Semantic search is a search algorithm that learns by itself to be able to understand the content of text. It uses structured data that it has collected from various sources for the purpose of searching for the text it needs to understand.

The NLP will be heavily used in 2021 for developing Semantic Search enabled applications. This process requires great understanding of NLP techniques and model development process. This process involves both NLP and natural language understanding (NLU), and these are two big system which will be use in 2021 for development intelligent Models in coming years. Data Scientists and Machine Learning professional must learn Semantic Search techniques in 2021 if they have to work on such projects.

The semantic search techniques can be used to develop intelligent chatbots, and semantic search engine for the website/applications.

According to a research the NLP system will grow by 100 times 2025, so in near future we have lot of growth opportunities for Data Scientists in this field.

  1. Use of NLP for Business Monitoring/Intelligence

Natural Language processing techniques can be used to keep an eye on the social posts to find out the sentiment of the customer through Sentiment Analysis.  The NLP techniques are not new but emerging fast due to availability of Big Data and data processing servers. Now, business can process vast amount of social data such as Twitter posts, Facebook posts, Emails etc. to understand the sentiments of the customers and use this analytics for business decisions.

Natural Language Processing is a branch of Artificial Intelligence that deals with the automatic processing of natural language such as text. NLP helps computers to find a match between a word and it's meaning. It allows them to understand a word or phrase that is difficult for humans to produce. It allows them to analyze the meaning of natural language such as text or voice by the computer program.  NLP techniques are used to develop computer AI model that can understand the meaning of text or voice and then action automatically. For example you can ask some question to the chatbot and chatbot will understand your query. After understanding your query chatbot will reply you back with the answer.

The use of NLP for business monitoring will see high trajectory in 2021, so IT professional working on the machine learning and artificial intelligence field should learn NLP techniques to help business in achieving their goal.

 The Natural language processing techniques can use to analyzing vast amount of text data and to generate the intelligence out of the data. This can be used for BI intelligence, customer sentiments, text abstraction and many such activities. The use of Natural Language processing will expand very fast in 2021.

  1. Use of Supervised learning and unsupervised learning in NLP

There are many ways machine learning can be used in NLP, the use of both supervised and unsupervised learning will increase very fast in 2021. The supervised and unsupervised learning can be used for developing machine learning/AI models, which can be used for Natural Language processing for vast amount of companies data.

  1. Use of reinforcement learning for training NLP models

The reinforcement learning is one of the most important types of machine learning, where agents are used to decide what to perform the given task. The reinforcement learning can be used in NLP for model training and develop better model for business use. It is also used fine-tuning the model.

The use of reinforcement learning in NLP will increase fast in 2021 and beyond due to promising results, when model is trained/fine-tuned with the use of reinforcement learning.

  1. Use of Deep learning in NLP

The Deep learning techniques such as Recurrent Neural Networks (RNN’s) can be used in NLP for accurate text classification. The use of RNNs will increase very fast in 2021, 2022 and beyond. These techniques can be used successfully for document classification, document summarization, document similarity and entity tagging. We would suggest Data Scientists to learn the use of RNN for Natural Language processing.

  1. Use of transfer learning for fine-tuning pre-trained models

The transfer learning is one of powerful machine learning techniques where pre-trained model is used as stating point and then re-trained for second task. In case of Natural language processing pre-trained model trained for sentiment analysis, text classifications, and so on can be used as starting point. The pre-trained model can be re-trained for increasing the accuracy for certain task. This process also saves a lot of computing power as previously trained model can be used. In coming years we will see tremendous use of transfer learning in NLP and other machine learning projects. Data Scientist should learn transfer learning techniques as they might have to work on such projects in near future.

  1. Use of NLP in customized product recommendations

The NLP techniques can also be used for product recommendations to the customers visiting an E-Commerce website. Here NLP will understand the product description, user’s previous browning history and other data for the recommendation of products to the customers. This way E-retailers might increase their sales. In coming years we will see the heavy use of NLP in product recommendations.

  1. Use of NLP for development of chatbots and virtual assistants

Natural Language Processing (NLP) is powerful machine learning techniques in the hand of Data Scientist  for developing computer model that understand the text intent. Based on the understanding of the text data chatbots can even reply back.

So, NLP can be used for development of chatbot and virtual assistant applications. As per the business predictions the chatbot market will grow to US$9.4 billion by 2024. The chatbot market was worth $2.6 billion in 2019. So, we will see very high use of NLP techniques for developing highly intelligent chatbot applications.

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Author: Deepak Kumar

Deepak Kumar is Science Graduate from Delhi University with more than 18 years of experience in Technical field. Currently his interest lies in researching for Media and Journalism field. He has years of rich experience in various technological fields. With a background in Science and Media field, Deepak has been offering services in the media houses and technical research. He has worked as director and chief in many companies. As a technical writer, editor and reviewer, he is offering services to many research organizations, media houses and online educational portals.

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