In recent years, businesses, brands and individuals have become increasingly dependent on technology to help them complete their daily tasks more efficiently. Artificial intelligence is playing a major role in this trend because it’s essentially the backbone of many assistive technologies. In the home, office, classroom and beyond, people may not even realize how often they’re interacting with AI-powered solutions.
For instance, estimates suggest that over 36% of the US population regularly uses voice assistants like Siri, Alexa and Google Voice. A form of artificial intelligence, natural language processing (NLP), powers each of these tools. NLP enables computers and other software programs to interpret and understand human language to complete specific tasks. In order to respond appropriately to human language and commands, however, a computer must also use a form of data science known as natural language understanding. By looking at the ins and outs of natural language understanding (NLU), it’s possible to gain a clearer picture of the role it plays in natural language processing and artificial intelligence. Here are the basics of NLU and its use cases for everyday life.
Table of Contents:
- What is Natural Language Understanding?
- How Does Natural Language Understanding (NLU) Work?
- What is the Order of Steps in Natural Language Understanding?
- What is the Difference Between NLP and NLU?
- Natural Language Understanding Examples
What is Natural Language Understanding?
Natural language understanding refers to the interpreting of data received through natural language processing. NLU is necessary for the technology to develop an appropriate response or to complete a specific action. Information like syntax and semantics help the technology properly interpret spoken language and its context. NLU is what enables artificial intelligence to correctly distinguish between homophones and homonyms. It also allows the technology to read subtle changes in intent and sentiment.
For instance, imagine that a person asks Siri to find a recipe for chocolate mousse. NLU allows the AI to understand that the individual isn’t talking about the animal “moose” but rather the dessert, “mousse.”
How Does Natural Language Understanding (NLU) Work?
It’s one thing to know what NLU is, but how does natural language understanding (NLU) work on an everyday basis? NLU is a form of data science that reads and analyzes the information gleaned from natural language processing. Additionally, it relies upon specific algorithms to help computers distinguish the intent of spoken or written language. NLU is also helps computers distinguish between and sort specific “entities,” which function somewhat like categories.
NLU mines spoken and written language for its most important components in order to trigger a specific action. When you ask your virtual assistant to turn on smart lights, for example, NLU enables your device to respond appropriately. Without the added context provided with NLU, your device might be able to roughly understand what you’re saying. However, it would not actually be able to put that understanding into action.
What is the Order of Steps in Natural Language Understanding?
Effective natural language understanding cannot take place in a vacuum. Systems must constantly work to better understand language by taking in information from a wide range of sources. This process helps to contribute to the ongoing evolution of the technology. Here is a breakdown of the steps involved in natural language understanding and the roles each of them plays.
Assessing data quality
If the data AI is analyzing is unclear or low quality, your final result is likely to be less accurate. If your objective is to help teach a device to correctly analyze and understand human language, it’s important to communicate clearly and efficiently.
Cleaning the data
If you notice substantial errors in the data you are using for the NLU process, you’ll need to correct those errors and improve the quality of the data.
Processing the data
At this point, the software will process the data and break it down into segments and categories that are easier for the computer to understand.
Modeling the data
Next, the segmented data will generate a type of language model to help computers learn about the probability of certain words being used in the same sentences or in specific contexts.
Analyzing the data
Once the data informs the language model, you can analyze the results to determine whether they’re sufficiently accurate and comprehensive. If the results are unsatisfactory upon analysis, you’ll need to adjust the input data before trying again.
Visualizing the data
If you’re satisfied with the analysis of your results, you may wish to visualize the data in some form of chart or graph. This step can make it easier to interpret the data. Also, the graphs can serve as a convenient reference tool down the line.
Operationalizing the data
Once the software achieves your desired rate of accuracy, you can implement the NLU process into your desired form of technology for consumer use.
What is the Difference Between NLP and NLU?
In the most basic sense, natural language understanding falls under the same umbrella as natural language processing. The two processes complement each other to help create software solutions that are capable of serving unique purposes.
In natural language processing, AI software like automatic speech recognition (ASR) software supports data intake. NLP enables the software to string together the spoken words to establish what the user was trying to communicate. From there, it’s the job of NLU to actually interpret the data in order to formulate the correct response.
Natural Language Understanding Examples
Perhaps the easiest way to answer the question, “What is natural language understanding?” is by exploring some examples of how this process shows up in the technology and tools we use every day.
When you ask Siri to call a specific person, NLP is responsible for displaying the text of your spoken command on the screen. NLU then interprets that information and executes the command by dialing the correct phone number.
If you ask Alexa to set a 10-minute timer, the device will use natural language understanding to figure out the end result you are seeking and then initialize the process of setting the actual timer.
When you’re typing a sentence on your phone, and the keyboard suggests a word you may intend to type next, NLP and NLU are working in conjunction with one another. NLP receives the data you input in the form of text messages, and NLU uses that information to suggest which word you are most likely to type next in the sequence.
NLU-Powered Assistive Technologies from Verbit
Verbit is one of the world’s foremost providers of assistive technologies like captioning, transcription, translation and audio description that boost efficiency and offer improved accessibility in a wide range of professional and educational environments. Verbit combines the efficiency of artificial intelligence with the expertise of professional human transcribers to offer captions and transcripts with accuracy rates as high as 99%.
With Verbit’s advanced AI platform and seamless software integrations, users can improve the quality of communication in person and online. Reach out today for a quote or to learn more about how Verbit’s solutions are helping brands and institutions offer more inclusive experiences.