Natural language processing

5 recent trends in Natural Language Processing you need to know

5 trends in Natural Language Processing that you need to know

 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 we sometimes don’t even notice how AI solutions facilitate our life. 

 AI is constantly evolving, especially over the last several years, it will continue to do so. Realizing the potential of this amazing technology that embraces machine learning, image and optical character recognition, natural language processing (NLP), etc., many 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 continue 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. NLP also includes 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 just about words, but body language, the intonation of an individual, and context, this task is very 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 much more.  Gartner predicts that by 2020 virtual customer assistants 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 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 a given text, computers manage to interpret the language and define a user’s intent. 

 That’s the main difference between speech recognition which 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, provide instant answers, and even direct the conversation. Directory services that assist people in finding the desired information, resolving their issues, and more. Such products will allow companies to dramatically improve customer service and automate manual operations.

 Due to the deep and correct language interpretation, machine-to-human communication will reach a completely new level. Business processes like data collection and analysis, data vetting, and 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 the 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 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 starting point for making 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 (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. 

 Additionally, due to the determination of the conversation’s context, as well as the user’s intent and sentiment, machine translation will be dramatically improved. As a result, machines are expected to replace humans in translation operations in the next 5-10 years.

4. Natural language content generation

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

 Even today, computers can prepare product descriptions for e-commerce stores. Although it still needs human validation, soon they will be able to autonomously generate articles and other types of texts in a given area and with results that are indistinguishable from human counterparts.

5. Cross-platform NLP usage

 Another important prediction for NLP for 2020-2025 is the creation and ubiquitous integration of cross-platform solutions with a single voice assistant that works across 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 be able to use the same voice assistant on all his or her devices with no loss of quality or accuracy.

6. Semantic search

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

 This way, it will no longer be necessary to spend a lot of time on information searches and analytical research as this process gets reduced from hours to minutes. Therefore, industries that deal with large amounts of data that include insurance, banking, healthcare, pharmaceuticals, and more will benefit from using semantic search applications.

Closing thoughts

 Natural language processing solutions are becoming widely introduced in different industries. Enabling automated data search and verification, machine translation, content generation, and human language understanding, NLP provides businesses with numerous advantages. With the growth of NLP technology, we’ll see a considerable increase in human-to-machine interactions. 

 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, feel free to leave your comments below. 

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