Have you ever sat in front of your computer, unsure of what actions to take in order to get your job done? If you’ve ever wished that you could just talk to it and have it understand what you say, then you’re in luck. Thanks to natural language understanding, not only can computers understand the meaning of our words, but they can also use language to enhance our living and working conditions in new exciting ways.
What is natural language understanding (NLU)?
Also known as natural language interpretation (NLI), natural language understanding (NLU) is a form of artificial intelligence. NLU is a subtopic of natural language processing (NLP), which uses machine learning techniques to improve AI’s capacity to understand human language.
Examples of NLU technology at work include:
- Automatic language translation: Nowadays, one can use services like Google Translate and Microsoft Translator to seamlessly translate text into hundreds of languages.
- Answering questions: When NLU is used in tandem with speech recognition software, AI can understand verbal communication. For example, one can ask the AI on their phone about tomorrow’s weather, and through NLU it will gather the information and relay it to us.
- Virtual assistant technology: Devices such as Google Home and Amazon Alexa implement NLU to provide their users with an all-encompassing solution to digital assistance.
What are the differences between NLU, NLP, and NLG?
Natural language understanding (NLU) and natural language generation (NLG) are both subsets of natural language processing (NLP). While the main focus of NLU technology is to give computers the capacity to understand human communication, NLG enables AI to generate natural language text answers automatically.
When a computer generates an answer to a query, it tends to use language bluntly without much in terms of fluidity, emotion, and personality. In contrast, natural language generation helps computers generate speech that is interesting and engaging, thus helping retain the attention of people. The software can be taught to make decisions on the fly, adapting itself to the most appropriate way to communicate with a person using their native language.
Are NLP chatbots any better?
How does natural language understanding work?
Natural language understanding implements algorithms that analyze human speech and break it down into semantic and pragmatic definitions. NLU technology aims to capture the intent behind communication and identify entities, such as people or numeric values, mentioned during speech.
Natural language understanding software doesn’t just understand the meaning of the individual words within a sentence, it also understands what they mean when they are put together. This means that NLU-powered conversational interfaces can grasp the meaning behind speech and determine the objectives of the words we use.
One of the main advantages of adopting software with machine learning algorithms is being able to conduct sentiment analysis operations. Sentiment analysis gives a business or organization access to structured information about their customers’ opinions and desires on any product or topic.
The roles and customer outcomes of conversational AI
This specific type of NLU technology focuses on identifying entities within human speech. An entity can represent a person, company, location, product, or any other relevant noun. Likewise, the software can also recognize numeric entities such as currencies, dates, or percentage values.
Why is natural language understanding important?
Natural language understanding gives us the ability to bridge the communicational gap between humans and computers. NLU empowers artificial intelligence to offer people assistance and has a wide range of applications. For example, customer support operations can be substantially improved by intelligent chatbots.
What are the steps in natural language understanding?
While NLU processes may seem instantaneous to the casual observer, there is much going on behind the scenes. Data must be gathered, organized, analyzed, and delivered before it is made functional.
A typical natural language understanding procedure contains the following steps:
- Data quality assessment: Machine learning algorithms will only learn as much as a data set allows them to. It is important to consider the quality of the data being analyzed and set one’s expectations accordingly. For instance, if one only runs sentiment analysis on data from a single location, it would be foolish to expect the results to reflect the opinions of an entire country.
- Cleaning the data: When the algorithms first interact with a data set, they are fed a convoluted mess of unstructured data. Not only will a considerable amount of data be superfluous, but the algorithms must also contend against misspelled words, idiomatic expressions, and grammatically incorrect sentences. For example, a simple “yes or no” question can contain many variations in the way people answer. The word yes can be replaced by “yeah”, “ye”, or “yass”. To obtain optimal results, an algorithm must be able to learn how to discern user intent.
- Processing: When the data has been polished as much as possible, then it is time to process it. This means taking out words that may be redundant for analysis, such as articles, prepositions, and other frequently used words. This way, what is left can be tokenized into individual elements a machine can read and interpret.
- Modeling: In natural language processing, a model is an organizational structure that represents the patterns found in data. A common model is the “bag of words”, which quantifies the number of times each word is mentioned and represents that value as a vector.
- Analysis: Once data has been modeled, then the results can be analyzed to understand the initial output quality. As previously mentioned, the quality of the results will depend on the data set being used.
- Visualization: Visual representations of data allow the software’s end users to understand the conclusions gathered by NLU technology. By displaying the data in chart form, one can ensure it will be easy to transmit to key individuals who are unfamiliar with NLU specifics.
- Operationalization: This is the process of gaining valuable insights and conclusions from a body of data. There are many ways to implement this final step. Examples include taking the data for further upstream analysis or using it for a specific purpose, such as market research. Through additional operationalization procedures, one can build a seamless method of gaining value from NLP technology. For instance, tech giants like Google and Amazon use topic modeling when delivering product data, providing automated filters and other QoL upgrades.
What capabilities should your NLU technology have?
The core capability of NLU technology is to understand language in the same way humans do instead of relying on keywords to grasp concepts. As language recognition software, NLU algorithms can enhance the interaction between humans and organizations while also improving data gathering and analysis.
The capabilities of natural language understanding solutions include:
- Interactive voice response (IVR) and message routing: NLU-powered IVR technology is able to process a person’s voice, convert the words to text, and run algorithms on its grammatical structure to understand intent. Dreamt by science fiction and made real by science, this technological advancement allows computers to understand what we say, when we say it.
- Text categorization and classification: By using NLU technology, a system is able to analyze a large body of text and gather useful data based on predefined categories. This is useful to filter data. Furthermore, NLU can be used to automatically summarize text, taking large volumes and reducing them to easily-digestible information tidbits.
- Intelligent customer support: One of the most common implementations of automatic language detection is the creation of chatbots. While most run-of-the-mill chatbots are only reading out of a script, NLU technology can give a chatbot the capacity to really know what they are talking about. These chatbots serve as digital assistants to professionals and customers alike.
- Machine translation: Also known as machine learning, this branch of AI development allows computers to learn and adapt based on patterns they have been trained to understand. Furthermore, machine translation enables computers to generate natural language text and even translate to and from other languages on the fly. NLU-leveraged statistical machine translation (SMT) can parse human speech through rule-based translation systems. There are many methods for this, like translating word-for-word, mapping sentences in one language into another, or using syntax as a basis for translation.
- Data capture: This is the process of gathering information about objects, people, and events. Combined with IVR technology, this allows people to verbally communicate critical information to computers.
- Conversational interfaces: Advances in conversational language understanding have led to the creation of devices, such as Google Home and Amazon Alexa, which allow humans to communicate with them using the natural way they speak. NLU allows these machines to segment words and sentences, recognize grammar, and use their knowledge of semantic roles to understand user intent.
The difference between a bot and conversational AI
Natural language understanding development services
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Botpress can be used to build simple chatbots as well as complex conversational language understanding projects. The platform supports 12 languages natively, including English, French, Spanish, Japanese, and Arabic. Language capabilities can be enhanced with the FastText model, granting users access to 157 different languages. Botpress is free, open-source, and able to run on the OS of your choice.
Natural language and dialog understanding