Natural Language Understanding (NLU)

What is natural language understanding (NLU)?

Natural language understanding (NLU), a form of natural language processing (NLP), allows users to better understand text through machine learning algorithms and statistical methods. These algorithms take language as an input and provide a variety of outputs based on the required task, including part-of-speech tagging, automatic summarization, named entity recognition (NER), sentiment analysis, emotion detection, parsing, tokenization, lemmatization, language detection, and more.

NLP is the parent category or overarching concept. It is a form of artificial intelligence (AI) technology that aims to make sense of human language. NLP helps computers to understand, interpret, and replicate human language characteristics.

NLP as a whole is a subset of AI. AI is a very general and broad space, with a number of different subcategories, including AI platforms, chatbots, deep learning, and machine learning. Deep learning becomes even more granular with further subcategories, such as NLP, speech recognition, and computer vision (image recognition). Each of these subcategories offers users a very different functionality that are all potentially valuable to businesses moving forward.

Types of natural language understanding (NLU)

Depending on what NLU is being used for or the industry a company is in, one of the different types of NLU will be utilized.

• Part-of-speech tagging: This type of NLU allows businesses to tag their text by parts of speech, such as nouns, verbs, and prepositions. This can help users understand the semantics of their documents and get a better understanding of their text data.
• Named entity recognition (NER): NER is a type of information extraction that can classify named entities (e.g., locations, people, and places) mentioned in unstructured text into predefined categories.

• Automatic summarization: It can be difficult for humans to summarize a large corpus of text quickly and correctly. NLU can be used to summarize text, giving researchers, business users, or anyone who needs a quick summary a leg up.
• Sentiment analysis: Texts can present positive or negative sentiments. Text data from customer service interactions can be analyzed using NLU, allowing customer service leaders to understand many interactions at various touchpoints and the type of sentiments they present.

Benefits of using natural language understanding (NLU)

NLU is not a technology that is only being used by AI practitioners and seasoned developers. Rather, it is being used in a host of software areas, providing tangible benefits for business users.

• Scale: Humans are great at analysis, but their analysis skills can break down when the amount of data is vast and when they need to produce results in record time. NLU-powered technology does not get stressed, pressured, or tired. It can analyze a (relatively) small amount of data or a large text corpus with ease, speed, and accuracy. This can be scaled across a business’ text datasets and various use cases.
• Discover trends: NLU can do a great job at finding trends and patterns in text data. Through word clouds, graphs and charts, and more, NLU can provide users deep insight into what is really happening beneath the surface.
• Empower non-technical users: Much NLU technology in the market is no-code or low-code, which allows non-technical users to get benefit from the technology. Gone are the days in which one needed to go to a data scientist or IT professional to get an understanding of language data.

Impacts of using natural language understanding (NLU)

 Many software areas are being positively influenced by NLU: 

• Chatbots: Conversational interfaces in different flavors, whether that be chatbots or intelligent virtual assistants (their more intelligent cousin), are greatly improved when injected with NLU. With NLU, users can have natural, human-like conversations with their technology, allowing them to get product details, procure HR information, book flights, and much more. Without NLU, conversational interfaces will basically have to suffice with menu bars.
• Contract analytics: Contract analytics software provides insights from extracted contract data to help companies keep terms consistent throughout all of their contracts. This type is supercharged by NLU.
• Market intelligence: Market intelligence software gathers publicly available information about companies and individuals from a variety of sources and uses it to create records or combine it with existing CRM data. Using NLU, it can better understand the information it pulls.
• Patent research: Patent research software, sometimes called intellectual property research software, helps manage the patent research and analysis process. This software can contain NLP-powered semantic search functionality to provide additional context to searches.
• Robotic process automation (RPA): RPA software utilizes bots to automate routine tasks within software applications usually performed by a company’s employees. Many solutions in this category provide NLP capabilities in order to understand text in documents and applications.

Basic elements of natural language understanding (NLU)

NLU solutions can vary in how they are packaged or delivered, but a complete offering will include the following elements:

• Ability to consume text data: With natural language at its core, this technology must provide the ability to consume various types of text data from different sources.
• Ability to make sense of text data: As an output, NLU must provide the end-user with something that makes sense of the text, such as NER, sentiment analysis, or automation summation.

Natural language understanding (NLU) best practices

In order to make natural language understanding work, follow these best practices:

• Have clean data: If data is full of irrelevant or incorrect data, expect results to be faulty. The best algorithm is only as good as the data it is presented with.
• Understand the data: NLU, like other varieties of AI, is not magic. As such, it will not automatically have all the answers to the questions you have yet to ask. Therefore, it is imperative that one has an understanding of the types of questions one is addressing, as well as the basic details of the text data in question. From that starting point, NLU can help with understanding patterns and trends.

Natural language understanding (NLU) vs. natural language generation (NLG)

Both NLU and NLG are subcategories of NLP.  The former takes text as an input and provides some sort of text-related insight as an output. The latter presents data (e.g., charts and graphs) in a digestible, natural language manner.