5 trends in Natural Language Processing



Coming up with a variety of use cases and opportunities, Artificial Intelligence is about to revolutionize the way we live and work. Yet today, it’s difficult to imagine our day without AI—Gmail filtering various messages, Alexa and Siri reacting to our voice and completing tasks, programs automatically checking our grammar… We are so used to all of this that sometimes don’t even notice how AI solutions facilitate our life. 

 

The thing is that AI is constantly evolving, especially for the last several years. And will be. Realizing the potential of this amazing technology that embraces machine learning, image and optical character recognition, natural language processing (NLP), etc., businesses are actively contributing to AI initiatives. What’s more, many companies are building their own AI teams that include data scientists, software engineers, DevOps, and business analysts.  

 

For now, NLP is undoubtedly one of the most promising areas of AI. In this article, you will learn what NLP technology is and how it works. We’ll also describe the key NLP trends that are expected to stay for 2020-2025. So, let’s get started!

 

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Natural language processing definition 

NLP is a subsection of linguistics, computer science, and AI that studies interactions between machines and natural languages. Natural language processing also concerns how to use machine learning algorithms for analyzing either human speech or text. 

 

By teaching computers to instantly interpret what an individual said or wrote and understand the actual meaning, it is poised to bridge the gap between people and computers. Since communication is not about just words but body language, the intonation of an individual, and context, this task is really challenging and the goal of NLP is to solve it.  

 

For example, NLP can be employed for creating human speech recognition systems, for machine-aided translation, data extraction and analysis, spam filtering, autocompletion, answers to the questions, predictive text input, and a lot more. By the way, Gartner predicts that by 2020 virtual customer assistants that use NLP will be adopted for 25% of customer service operations.

 

Just imagine how different industries—retail, banking, insurance, IT, healthcare, and many others—can take advantage of adopting natural language processing. NLP applications can include bots helping customers make purchases and providing consultations, programs taught to capture and process data, analyze and verify documents, conversational interfaces for computers, smart homes, smart cars, and so on.

 

As you can see, the number of possible use cases of NLP technology is almost infinite. Now, let’s take a look at NLP latest trends and predictions for 2020 and beyond.

 

Recent trends in natural language processing

1. The evolution of natural language understanding

Natural language understanding (NLU)—also called natural language interpretation (NLI)—is a category of NLP that is based on machine reading comprehension. By breaking down and analyzing the elemental pieces of human speech or given text, computers manage to interpret the language and define a user’s intent. 

 

That’s the main difference between speech recognition that aims to reprint language in real-time, and NLU that focuses on the determination of intent, sentiment, and context.

 

With the evolution of NLU, we’ll see the ubiquitous growth of its applications. Just imagine how it will be: systems that understand the meaning thanks to NLP technology, provide instant answers and even direct the conversation. Directory services that assist people in finding the desired information and  resolving their issues, and more. Such products will let companies dramatically improve customer service and automate manual operations.

 

Due to the deep and right language interpretation, machine-to-human communication will move to a completely new level. Furthermore, with natural language processing, business operations like data collection and analysis, data vetting, facts checking will be automated and errors—excluded.

 

2. Use of supervised and unsupervised learning together with NLP

In common speak, unsupervised learning is about providing an algorithm with input data as a training set and finding correlations. Once it understands the term in a given text and parts of human speech, unsupervised learning is able to correctly define mathematical relationships between them.

 

Supervised learning implies teaching computers with the help of data that has some pieces already tagged with the correct answers. By learning from data, it becomes possible to project the outcome of unforeseen information and fill in the gaps. It is noteworthy that supervised learning applies the results achieved during the process of unsupervised learning which enables it to provide quality data insights much faster.

 

At the moment, technological solutions employing natural language processing such as chatbots, robots, self-driving cars, decision-support systems, and forecasting systems can employ both supervised and unsupervised learning. 

 

Since this kind of AI enables companies and, in particular, data scientists, to discover patterns in big data, they get the ability to make predictions. Also, supervised and unsupervised learning can be used as a great base for taking successful decisions. 

 

Working in conjunction with NLP, it will become possible to take a deeper look at the meaning, context, and sentiment of the analyzed texts and produce better results.

 

3. Replacement of manual translation with machine translation

Thanks to transfer learning (it is a machine learning technique that allows using the experience gained in solving one problem to solve another, similar issue), we’ll see the appearance and distribution of multilingual translation in the near future. With the increasing use of speech recognition technologies and text analysis systems, computers will be able to instantly process and interpret the human language. 

 

In addition, due to the determination of the conversation’s context, the user’s intent and sentiment, machine translation will be extremely improved. With the evolution of natural language processing, machines are expected to replace humans in translation operations in the next 5-10 years.

 

4. Natural language content generation

In the future, the content will be mainly produced by machines. This is called natural language generation (NLG), a category of natural language processing that focuses on creating texts with the help of structuring and summarizing data. 

 

Yet today, computers can prepare product descriptions for e-commerce stores using NLP technology. Although now this operation still requires human validation, soon they will be able to autonomously generate articles and other types of texts in a given area and make it no worse than people. This is another field that is expected to be significantly transformed with natural language processing.

 

5. Cross-platform NLP usage

Another important prediction regarding natural language processing for 2020-2025 is the creation and ubiquitous integration of cross-platform solutions with the NLP-based voice assistant that works equally on various devices. 

 

For example, a conversational interface can be implemented in smart homes, self-driving cars, smartphones, computers, and other devices. Hence, an individual will get the ability to use the same NLP-enabled voice assistant on all his or her devices while the context of communication won’t be lost.

 

6. Semantic search

Semantic search is one of the most popular natural language processing trends for 2020. Including both NLP and NLU and aiming to define the key ideas of a certain text, it aims to quickly process multiple documents to define specific terms, insights, and requirements. 

 

This way, there is no necessity to spend a lot of time on information search and analytical research as this operation gets reduced from long hours to minutes. Therefore, industries that deal with large amounts of data that include insurance, banking, healthcare and pharmaceuticals, etc., will benefit from using NLP-based semantic search applications.

 

7. The improvement of speech recognition quality to the human level

Analysts predict speech recognition technologies will be substantially improved in the near future thanks to natural language processing. This will involve minimization of errors, recognition of what several individuals are saying despite different accents and a noisy environment. 

 

What’s more, with NLP, computers will soon be able to analyze a variety of sounds and emotional colouring of human speech. Therefore, the level of human language recognition will approach the level of people. And this all is becoming possible due to NLP technology. 

 

 
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Closing thoughts

Natural language processing solutions are becoming widely introduced in different industries. Bringing automated data search and verification, machine translation, content generation, and human language understanding, NLP provides businesses with plenty of advantages. 

 

With the growth of NLP technology, we’ll see a considerable increase in human-to-machine interactions. Furthermore, as NLP-based systems are predicted to replace humans in some tasks and processes, some existing jobs will almost disappear. As natural language processing tools can carry out some operations much faster and without errors, businesses will be able to extremely improve customer service, optimize costs, and boost efficiency.



If you would like to know how NLP and AI tools can be effectively used in your business and how they can solve your challenges, drop me a message or book a meeting. If you’ve enjoyed this article or want to share some thoughts about NLP, feel free to leave your comments below.

Bogdan COO
Bogdan
COO
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