What Is The Future Of NLP?

What Is The Future Of NLP?

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In today’s society, data is all around us. In fact, it is a commodity utilized by many businesses and organizations – with most of it being unstructured. What this means is that there is a need for Natural Language Processing (NLP). 

If you’re unfamiliar with this, essentially, it is an AI subset that enables computers to converse with humans. Here, AI has the ability to ‘understand’ human speech or text and deliver a response that meets the user’s requirements. 

You may be wondering, what is the limit to NLP and what a future with NLP looks like. Well, you’ve come to the right place to find out. 

With this in mind, this article will explore everything you need to know about NLP and the future of the application – exploring its development and impact on future businesses.  

Let’s get straight into it! 

What Is Natural Language Processing?

Natural Language Processing (NLP) refers to a branch of artificial intelligence, computational linguistics, and computer science with a focus on computer and human interactions. 

The goal of this application is to enable computers and humans to communicate in a natural way, for instance, through language as opposed to a series of symbols. 

NLP encompasses different functions, including speech synthesis, speech recognition, understanding natural language, document retrieval, information retrieval, and machine translation. 

Below, we have outlined 3 predictions when it comes to the future of NLP. 

1. Enhanced Conversational AI Tools

One subdivision of NLP is conversational AI. This is the application that can ‘understand’ and respond to human interactions. Below, we have outlined some applications that use this technology. 

  • Intelligent virtual assistants (IVAs)
  • Chatbots
  • Digital workers (digeys)
  • Voice bots (voice assistants)

Thanks to improvements in NLP models (Intent recognition), conversational AI is able to better understand the nuances of human interactions. 

Plus, due to advanced natural language understanding (NLU), these tools can interact with humans more naturally. 

Below, we have outlined three terms that are sure to become more relevant in companies in the near future, these include:

Conversational Commerce

This refers to a marketing strategy that uses conversational commerce to enhance clients’ experience. 

Here, omnichannel tools are implemented which include mass messaging tools, live agents, and chatbots on various platforms, including:

  • Facebook Messenger
  • WhatsApp
  • Websites of companies 
  • Company call centers
  • Companies mobiles applications

This approach is ideal for businesses in the e-commerce, hospitality, and retail industries. Below, we have outlined some cases where conversational commerce can be used. 

  • Customer support – There are always going to be customer queries – from frequently asked questions to delivery details – therefore, conversational commerce can provide customers with automated responses. 
  • Visa eligibility screening – According to your personal information, these chatbots can access your eligibility for a particular visa. 
  • Product recommendations – Some users may be familiar with their particular problem but are unsure of the different ways to tackle it. Using a two-way communication approach, a chatbot can help solve this problem and provide the necessary information. 

Intelligent Automation

Here, intelligent automation may be implemented by companies to provide employees with digital workers where they can instruct them to complete various tasks as a result of conversational AI. 

Through intelligent automation tools, end-to-end automation is guaranteed. Likelike, these digital workers can work autonomously and continuously. Plus, they are ideal for increasing employee productivity. 

Below, we have outlined some of the most common features digital workers can implement in a workspace, these include:

  • Collecting data from ERP and CRM accounting tools
  • Writing and sending emails
  • Recruiting 
  • Visualizing and interpreting data 
  • Reporting

Conversational Banking

When it comes to conversational banking, this is the introduction of conversation commerce in the financial services industry. Interactions between customers and institutions are completed through:

  • Wealth management chatbots
  • Mortgage chatbots
  • Banking chatbots

As a result of conversation baking, businesses are now able to automate the following applications:

  • Document verification and collection when applying for a mortgage
  • Stock recommendations to users
  • Customer onboarding

2. Increased Investments In NLP

What Is The Future Of NLP?

According to reports, as of 2022, the market size of NLP is approximately $16 billion. In 2027, this is expected to rise to $50 billion – demonstrating a growth rate exceeding 25%. 

Further studies show that North America is the biggest market for NLP. While, on the other hand, East Asia invests in NLP solutions. 

Reinforced Data Quality And Availability 

Additionally, another factor that improves an NLP system’s capabilities is the particular data’s quality and availability. 

When it comes to enhancing the quality of data training, there is a range of data labeling tools that can influence and achieve audio data and text annotations. 

Those two tools in particular, when used together, result in the expansion of the NLP market. 

Development In Machine Learning Infrastructure 

AI chips make up the brains of the NLP models. Hence, the more powerful the chip, the more powerful the machine is when it comes to computational ability and human-like interactions. 

Those who design these AI chips’ are expanding the parameters of these processors, thus developing the NLP systems’ model size. 

While not all businesses are able to house such large models (this wouldn’t be a smart investment, either), when it comes to chip development, this positively impacts the NLP models’ general capabilities. 

Customer Expectations

Research conducted by Accenture demonstrates that over 75% of CEOs want to configure their approach to handling customer relationships entirely. This is in an effort to stay ahead of changing customer expectations. 

Therefore, since customers are expecting quick and accurate interactions from brands, businesses have to implement NLP models to combat this. 

3. Natural Language Generation Used To Create Text

Another sub-branch of NLP is natural language generation (NLG). This tool is already implemented by various marketers and content creators. 

However, it is predicted that more companies and businesses will implement NLP-driven content editors and automated text generation as it allows:

  • Companies gain approximately 60% of new customers due to content marketing. 
  • Companies can invest more in marketing. 

Plus, NLP has the ability to ease some marketer’s tasks, including:

  • Content paraphrasing – These tools can be implemented to polish content. Here, the AI can rewrite a given phrase and make it more user-friendly. 
  • Translation of content – It is beneficial for companies to engage with customers using their preferred language. Thanks to developments in NLP, high-quality machine translation can be implemented. 
  • Generating content – Original content can be created using AI tools – all you have to do is choose the topic. 
  • Editing content – This is already widely implemented through Microsoft Word where grammatical and vocabulary errors are flagged. With enhanced NLP models, content can be proofread. 
  • SEO advice – Your content can be optimized to appear on the first page of Google results. 

Final Thoughts

It comes as no surprise that data is all around us and is only increasing with technological advancements. One of which includes natural language processing. 

The aim of this application is to create natural interactions between machines and humans which can be implemented in a range of industries from finance to marketing. 

Hopefully, this guide has informed you about the future of natural language processing and what to expect. 

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