Sunday, September 19, 2021
HomeML & AITop 10 Natural Language Processing (NLP) Trends To Look Forward

Top 10 Natural Language Processing (NLP) Trends To Look Forward

AI and Machine Learning have gifted us marvelous things. NLP or Natural Language Processing is one of them. It is one of the most prominent applications of AI. We are using this technology in our day-to-day life without even knowing. Translators, speech recognition apps, chatbots are actually NLP-powered products. Tech giants like Google and Microsoft are making new developments in NLP every year. If you are an AI enthusiast, you should go deep inside NLP. Chill! We got you covered. Just go through the article, and know about the top NLP trends that most data scientists are talking about.


NLP is a skill worth learning. For that, you have to have an idea about AI, ML, ML algorithms, and metrics. Moreover, you have to know what type of NLP models today’s data scientists are working with. So, we have listed the top 10 NLP trends you can follow for future advancement.

01. Sentiment Analysis


For any brand, it is significant to know what people are thinking about their products. Social media is a massive platform to monitor people’s perspectives. But it will be tough to do the process manually. Hopefully, we have NLP. It automates the whole process. Now, you can extract people’s sentiments from comments and posts about a product on social media.

sentiment analysis-NLP trends

The process is called sentiment analysis. It analyzes people’s views, opinions, and outlooks about any topic. Market research has become more comfortable due to the process. If you want to start a business, use sentiment analysis and design your product according to people’s needs. There is less chance of failure of your product if you study people’s views using sentiment analysis.

02. Multilingual NLP


Multilingual NLP is a major NLP trend. Monolingual models can handle a single language, whereas multilingual models can handle several languages at a time. Translation of one language to another is an example of multilingual NLP. You can only detect English words using regular NLP models. But using multilingual models, you can identify words in English as well as in Spanish, French, and Portuguese.

Facebook introduced the M2M-100, a multilingual model that can process 100 languages without depending on English. Microsoft innovated a similar one, the Turing model. It is the largest model ever published, having 17 billion parameters. The model outperforms most of the available state-of-the-art models. These types of multilingual NLP have facilitated the exchange of feeling all over the world.

03. Chatbots and Virtual Assistants


Due to the COVID-19 situation, there has been a rise in customer support tickets in every industry. It is quite a challenge to handle all these tickets manually. Chatbots and virtual assistants are specifically trained to handle several customers at a time and in a more effective way. Operating customer tickets consumes a lot of time. However, chatbots relieve the agents from this task and allow them to concentrate on higher-value tasks.

Chatbots and VA

Companies now realize the importance and effectiveness of chatbots. To meet the rising demand, developers are bringing new features every day. Chatbots learn on the run. The more they interrogate customers, the more their efficiency increases. They can now handle complex conversations and do entirely new tasks without prior instructions.

04. Market Intelligence Monitoring


Keeping up to date with fast-changing industry developments and demands is very crucial. What was famous yesterday may not be in need tomorrow. NLP is an essential tool for surveillance and managing market intelligence reports to extract vital information for strategic growth. This NLP trend guides financial experts to analyze the market situation and make relevant decisions.

The monitoring process is already in use in many industries. Sentiment analysis is also used in this trend to know about product demand. In the future, businesses will highly rely on NLP in making further progressions. NLP has made the market monitoring process relatively easy.

05. Deep Learning in NLP


There was a time when light and shallow Machine Learning algorithms were used in NLP. However, developers are now incorporating deep neural networks in solving natural language processing problems. Traditional ML in NLP had some shortcomings. Deep Learning has removed these drawbacks and increased effectiveness.

DL in NLP

RNN, CNN, and recursive neural networks optimize NLP models and product attributes such as semantic role labeling, contextual embedding, and machine translations. Recurrent Neural Networks (RNN) are mostly used in NLP. They help the model to classify texts accurately. The use of RNN in NLP will soon become a trend among data scientists as it makes document classification much efficient.

06. Combination of Supervised and Unsupervised Methods


Training a model with labelled data is called supervised learning. On the other hand, training without any labell is unsupervised learning. In the case of training an NLP model, the combination of both methods results in betterment. Supervised learning is typically applied in topic classification. The model has to be trained several times to reach a satisfactory result.

Unsupervised learning has the ability to detect patterns. It groups objects based on similarity. When you use both the learning methods in NLP models, the performance of the model boosts. Developers especially use these types of models for text analysis. Supervised learning detects the complicated terms in a text and parts of speech, whereas unsupervised learning examines the connection between them.

07. Detecting Fake News and Cyberbullying


People always spread fake news on the internet. Following unreliable information may harm a person and business. You cannot just read an article and decide its fakeness in seconds. But NLP can. It can detect whether the news is fake or not within seconds. Thus, the method saves time and human effort and avoids the propagation of fake news.

Many websites and social media use NLP to detect cyberbullying. It has become a major NLP trend. Facebook, Twitter use Machine Learning classifiers to distinguish hate speech or offensive language. Developers have been working to stop cyberbullying by implementing NLP and make the internet a safe place.


Intelligent semantic search technology is a rising trend in today’s world. We always search for the meaning of a word or a sentence on the internet. Search engines show us the best translation. But there are cases where we need the inner meaning of a sentence. Translating the sentence by putting individual word meanings will not do in that case.

semantic search-NLP trends

To solve this problem, NLP has been applied in search engines. It is now possible to train the model with millions of documents. The model will provide semantically similar meanings. In earlier days, search engines looked for the literal meaning of the word. However, in semantic search, the meaning is placed based on the content origin of the word. This process has made our searching experience quite fruitful.

09. Transfer Learning in NLP


Transfer Learning is a famous Machine Learning method. Suppose you want to build a model. But you don’t have enough data. In that case, you can collect a similar type of model and train your model based on the previous model. This way of training one model from another model is called Transfer Learning.

If you use Transfer Learning, you don’t have to build your model from scratch. It saves a lot of time and effort. The only thing you need to do is fine-tune a pre-trained model. You can use this method in NLP. Developers can solve NLP tasks with limited data and time. That’s why it has become one of the top NLP trends in today’s world.

10. Customized Product Recommendation


The world is moving towards online business. In 2020, due to COVID-19, online markets became very famous. It is essential to analyze customers browsing patterns. Companies are using NLP techniques to analyze shopping trends and increase customer engagement. The product recommendation system is an application of NLP.

recommendation system

Basically, a product recommendation is a filtering method that attempts to identify and demonstrate the products consumers would like to buy. In recent years, recommendation systems have become widely popular. They are used in a number of fields, including movies, news, books, research papers, music, and other items.

What Next?


It is crystal clear that AI and ML are going to rule the next era. Every industry will have a taste of AI. A business must use NLP to know people’s insights about their product. Moreover, you cannot expect to get a safe and scam-free website without NLP. From the detection of spam emails to speech recognition, NLP is everywhere. To make yourself acquainted with it, we listed the top NLP trends that most data scientists are researching and most businesses are applying in their product.

We have tried to include the most trendy ones. The article will be beneficial to beginners. Still, there may be some shortcomings. Let us know your insight about the article. And keep yourselves updated by regularly going through our website.

LEAVE A REPLY

Please enter your comment!
Please enter your name here

spot_img

Latest Post

Must Read