Today, inordinate volumes of information are captured in the form of natural human language: emails, social media messages, articles, and more. We can more quickly make sense of and use that information today with the help of computers. But computers alone aren’t enough – they rely on natural language processing (NLP) to extract meaning from information in the form of human language. In simple terms, NLP allows computers to understand both human speech and text.
The History of NLP
Before we dive into how NLP works and how businesses are making use of it, let’s briefly walk through its history.
Alan Turing kickstarted the conversation in 1950 when he published a paper describing a way to test whether a machine can think. This was closely followed by the Hodgkin-Huxley model, published in 1952, which showed how the brain uses neurons to form an electrical network. Combined, these inspired the idea for Artificial Intelligence (AI) and Natural Language Processing.
The first application of NLP was in the 1950s when it was used to help break enemy codes during World War II. In this capacity it was used simply for machine translation. While these translations from Russian to English weren’t successful, NLP was successfully applied in the mid 1960s in a chatbot called ELIZA. Developed by a researcher at the Artificial Intelligence Laboratory at MIT, ELIZA was meant to enable a conversation between a computer and a human. Using pattern matching and substitution, ELIZA could hold a limited conversation.
In the 1980s, NLP evolved by leaps and bounds – and gave us the NLP we know today – thanks to the introduction of statistical NLP and machine learning-driven algorithms for language processing.
Today NLP is behind familiar devices like Alexa and tools like chatbots. As NLP better understands how we communicate, it’s becoming more natural to talk to computers as we do with other humans.
How NLP Works
It’s no small feat for computers to understand human language. Consider all the languages, dialects, figures of speech, mispronunciations, speech impediments, and other factors that can complicate comprehension. Computers have long struggled to deal with messy, unstructured, complicated tasks – until the advent of Artificial Intelligence. AI-powered technologies are enabling computers to learn to process and extract meaning from all forms of human language.
Underlying this capability is natural language processing. Here’s how it works.
NLP enables computers to understand human speech and text by using machine learning. While computers are good at processing structured data, they have long struggled to process unstructured data (i.e., human language). With AI and machine learning, computers can be trained on language rules. With a basic understanding of language conventions, computers can quickly and effectively answer simple written questions.
When it comes to responding to voice prompts, computers must also call upon speech recognition. By analyzing the soundwaves and combining it with an understanding of language conventions, machines powered by NLP can recognize and respond to human voices.
While early NLP mechanisms were based on strict rule-based algorithms, they are now based on modern algorithms that call upon statistical modeling. These models introduce more flexibility and accuracy into the processing, such as by enabling machines to better analyze sentiment.
When a person engages NLP, whether through text or speech, whatever the person says or asks is considered an “utterance.” AI analyzes the utterance to determine what it is being said. First, it interprets the intent. For example, “How many sales are we forecasting this month?” The intent in this case is understanding the revenue forecast. Next AI interprets the terms (aka entities) modifying the intent. In this case, the term or entity is “this month.” So, to accurately answer the question, NLP examines the utterance, and extracts the intent and entities.
The Difference Between NLP, NLU and NLG
The terms NLU (Natural Language Understanding) and NLG (Natural Language Generation) are sometimes used interchangeably with NLP but they are subsets of Natural Language Processing.
While NLP makes it possible for people and machines to talk to each other “naturally,” NLU deals specifically with comprehending unstructured data and NLG transforms structured data into natural language to produce different variations of a sentence or statement. In this way, both NLU and NLG underpin NLP’s ability to ingest what is said or written, comprehend its meaning, determine the appropriate response, and respond in a way the person will understand.
Gartner defines NLU as “the comprehension by computers of the structure and meaning of human language (e.g., English, Spanish, Japanese), allowing users to interact with the computer using natural sentences”.
How Businesses Use NLP
Whether you realize it or not, NLP is becoming integral to workplace operations. It’s behind predictive suggestions on our mobile devices and voice-activated assistants, powers the spam filters in our email, and much more. Here are specific examples of NLP in use.
1. Fueling data-informed decisions
By asking questions of NLP-powered applications, business users can far more quickly understand insights from mountains of data. Rather than sift through the data, workers can ask a question of the assistant, which can sift through the data much faster than a human.
2. Analyzing sentiment
Impressively, machines can understand the emotional context behind the words a person speaks or types. This sentiment analysis helps companies to gauge whether a person is upset, weigh the impact of customer opinions, monitor their reputation, and understand overall customer satisfaction, to give a few examples.
3. Producing summaries
Unlike humans, NLP can easily extract key information from reams of information. With this capability, they can power tools that automatically summarize lengthy documents or “speeches” into short summaries.
4. Answering questions
Perhaps one of the most common ways we all use NLP is when we interact with chatbots. These tools can streamline business processes, and more quickly help people arrive at answers.
How SKAEL Harnesses NLP, NLU and NLG
NLP, NLU and NLG are three of the secret sauces SKAEL uses to help its Digital Employees understand what the end user is asking of it. NLP powers the interactions between Digital Employees and your human employees. You see NLP working when communicating with your Digital Employee via a communication platform such as Slack, Microsoft Teams, Google Chat, webchat, etc.
While NLU allows Digital Employees to understand what the end user’s intent, NLG automatically produces multiple variations of the end user’s request – such as by dropping letters, randomly capitalizing letters, rearranging words, etc. This enables the Digital Employee to more quickly learn the nuances of your employees and more quickly respond to their requests.
While other automations – like RPA – call upon NLP, SKAEL embeds this processing capability directly in its Digital Employees. In RPA-powered solutions, you likely need to add NLP separately. Also unlike RPA, Digital Employees are easy to use and deploy, making them the undiscovered gem in the hyperautomation schema.
Ready to experience the power of our NLP-powered Digital Employees? Sign up for a demo.