An Introduction to Natural Language Processing

Businesses can use natural language processing to process text-heavy data, glean valuable insights, streamline business operations, and enhance the customer experience. This blog defines natural language processing, outlines applications and examples, and details how businesses can use the solution to streamline business operations.  

April 07, 2022

Can you have a natural-sounding conversation with a machine? 

Modern technology attempts to bridge the gap between human-to-human and human-to-machine communication. This is called natural language processing. It’s commonly found in smartphones and smart speakers, but it’s also catching on in business customer service practices.

What is Natural Language Processing?

Natural language processing is a branch of artificial intelligence (AI) that helps computers understand, interpret, and manipulate human language. Natural language refers to just that: spoken or written human language. The technology’s use of AI allows it to interpret what a person says or types. Moreover, natural language processing is a learning algorithm that identifies and grasps various patterns. It then makes decisions or predictions independently, relying on collected data and sometimes human input.

How Does Natural Language Processing Work?

The technology uses syntax and semantic analysis to process natural language. Syntax is the arrangement of words in a sentence to make grammatical sense. Semantics is the meaning behind those words and phrases. Therefore, natural language processing works through the combination of these grammatical tools and AI. This then results in an intelligent virtual assistant (IVA) that understands context and handles complex human interactions, but more on that later.

Natural Language Processing Applications

There are several ways natural language processing enables the communication between technology and humans. Some examples include the following:

  • Machine translation is how a computer uses natural language processing to translate text from one language to another without human intervention. Machine translation is particularly useful in business customer communication strategies because it facilitates interactions, allows companies to reach broader audiences, and understands foreign documentation quickly and cost-effectively.
  • Sentiment analysis is when the natural language processing algorithm determines the sentiment or emotion behind a text. Humans often use sarcasm and irony, which can make it difficult for computer systems to understand. However, sentiment analysis recognizes subtle nuances in emotions and opinions ‒ and determines how positive or negative they are. Businesses can periodically perform sentiment analysis to gauge what customers like and dislike about the company; perhaps they love a new feature but are disappointed in the customer service. Those insights inform strategic planning because they demonstrate weak areas that need improvement.
  • Information extraction uses natural language processing to pull the most important words (e.g., names, places, dates, etc.) from the text. The computer can sift through customer support tickets and identify specific data (e.g., order numbers and email addresses), without opening and reading every ticket. Combined with sentiment analysis, information extraction adds an extra layer of insight for a business by identifying words customers use most often to express sentiment toward the product or service.
  • Question answering is a system that automatically answers questions posed by humans in a natural language. Typical question answering systems follow predetermined rules. On the other hand, natural language processing tools such as chatbots and virtual assistants can learn from each interaction and understand how to respond. As such, they learn from interactions and improve over time. 

 

Natural Language Processing Examples 

Everyday natural language processing examples include search engines like Google, email filters, speech-to-text dictation software, and intelligent voice assistants like Siri or Alexa. The technology is also popular among enterprises through virtual assistants and chatbots. Virtual assistant natural language processing examples include essential customer support tools such as IVAs, interactive voice response (IVR), and AI chatbots.

Virtual assistants that leverage natural language processing streamline customer service to improve customer experiences. For example, businesses use natural language processing in contact centers to analyze large volumes of text and spoken data from customer support tickets and phone calls. The intelligent tool supports the customer’s request and also shares valuable insights about improving the customer experience.

IVAs

As teased above, IVAs are one example of natural language processing-enabled technology. One of customers’ biggest misconceptions about virtual assistant technology is the perception that a “robot” can’t solve their sophisticated issues. Or the caller doesn’t think their problem fits the IVA’s pre-programmed options. Natural language addresses these common concerns by letting the caller speak or message freely to a computer and receive timely resolution as if speaking to a live agent. The business can train the IVA via the natural language processing solution to learn from previous interactions. Also, IVAs can pick up the caller’s intent, tone, and emotions and come up with solutions based on the analysis of that data. 

IVR

IVR is an automated phone system technology. It allows incoming callers to access information via a voice response system of pre-recorded messages without having to speak to an agent. Most IVRs utilize menu options to route calls to specific departments or specialists. Still, some contact centers use natural language processing to allow callers to say what they’re calling about (i.e., checking an account balance) in various ways. The IVR can understand the caller even if their words are vague.

An IVR helps businesses increase customer satisfaction and improve contact center operations. For example, IVRs reduce long hold times during times of high call volume by enabling the caller to find answers and perform simple tasks on their own. Natural language processing in an IVR is limited, however. While it’s more helpful than a list of menu options, an IVA is better for conversational interactions.

AI Chatbots 

Chatbots communicate with a user automatically without help from an agent via voice or text. Artificially intelligent chatbots mimic human-like traits and responses. Natural language processing works behind the scenes to enable these chatbots to understand the dialects and undertones of human conversation. 

The Benefits of Natural Language Processing 

Natural language processing combined with AI creates knowledgeable virtual assistants that can respond to nuanced questions and learn from every interaction to create better-suited responses in the future. These capabilities offer unique benefits to customer service and experience. Below are three benefits of integrating natural language processing.  

Answer on Self-service Demands

73% of customers prefer to solve problems themselves instead of requesting the support of an agent. Natural language processing-enabled technologies such as IVAs, IVR, and AI chatbots manage common challenges customers face without a live agent. This way, customers gain greater autonomy over their interactions with the business and the option to solve problems quickly at any time they need. For example, intelligent assistants can support a caller wanting to pay a bill themselves or check their account balance.

Reduce Wait Times with Task Automation

With natural language processing, contact centers can answer basic inquiries, reduce wait times for customers, and free up human agents to manage more complex service needs. For example, if a customer calls to manage a subscription, they will follow an automated guide to enter all of their information. IVAs, IVR, and AI chatbots use natural language processing to respond to open-ended prompts and recognize keywords and phrases to move the customer along on their journey. After entering all of their information, the caller is then connected to a live agent.

Personalize Customer Service

Natural language processing can pick up on unique communication needs and customer tendencies. Businesses can increase customer satisfaction and retention by providing personalized and contextual customer service based on previous interactions. Natural language processing works in two ways. It makes machines sound more natural, which makes the experience for the caller more comfortable.

How Intrado’s Mosaic Platform uses Natural Language Processing

Mosaic’s IVA generates personalized responses to users. Mosaic processes natural language requests using Google’s natural language processing models. These processing models interpret situational context, allowing the tool to handle a more complex range of questions and interactions. The solution resembles human speech and can understand queries with spelling and grammatical errors, slang, or potentially confusing language. With Mosaic, users can rest assured that their information is accurate. 

Natural language processing plays a vital part in helping businesses communicate with customers effectively. Companies can use natural language processing to enhance customer experience through intelligent virtual assistants that automate and personalize service.

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