NLP, NLU, and NLG: Does AI Really Understand Human Language?

First Posted: May 12, 2020 12:48 PM EDT
NLP, NLU, and NLG: Does AI Really Understand Human Language?

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Technology has made leaps and bounds in the realm of AI and machine learning. Compared to years before, when it was almost impossible for computers to understand human (natural) language, AI systems can now interact in chat conversation and predict user intent. There are branches of AI that assist specifically in the analysis and interpretation of natural language, and these are Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG).

Because these three components are part of a larger process that allows for the reproduction of human communication in computers. Discussing these three concepts will help in understanding the whole process and determining whether they really understand human language or not.

Natural Language Processing (NLP)

Speech recognition is integral to NLP because it's the first step toward understanding natural language and carrying out commands using that language. NLP relies on a library or database to determine a set of rules that will help a computer or system interpret what a user says or types into a search bar or other entry field. What's good about this database is that it can be customized to meet the needs of your business. 

NLP is the integration of data science and human language because neural networks play an important role in its execution. Neural networks allow for text classification, answering questions, sentiment analysis, and similar areas. They make it easier to process the data required to understand natural language without complex programming. NLP also adapts to language patterns, so it becomes smarter with regular use, providing more tailored results as it gains more "practice."

Natural Language Understanding (NLU)

NLU is a smaller process done within NLP; it's the breaking down of natural language into smaller, more digestible chunks. This process makes it easier for computers to make sense of verbal human conversation or conversations via chat or search, sometimes even being able to perform sentiment analysis.

Computers break down natural language by analyzing its two aspects-semantics and syntax. This means that it goes beyond determining proper form but also goes so far as to interpret context. To make this possible, NLU compares words, sentences, or paragraphs with similar examples to find similarities and determine errors. It also breaks down text into parts of speech, sentence structures, and morphemes or the smallest part of a word that's understandable.

NLU helps in understanding the natural way humans talk because it's designed to interpret unstructured data. Natural language is far from structured and can be quite ambiguous. Mistakes are also common in natural speech and different people tend to say the same thing in different ways. This is the true power of NLU-understanding and making sense of the unstructured, often figurative, way humans converse.

Natural Language Generation (NLG)

NLG, in simpler terms, is the final step in the process of understanding and interpreting natural language. Based on the data gathered by the system, NLG automatically processes and reproduces them to a linguistic equivalent-something that humans can understand and use. It can actually create sentences and entire paragraphs, effectively telling a story similar to what humans do. It saves time in the analysis and reporting of complex data sets.

NLG focuses on making sense of textual content by combining analytic output with context gathered from narratives. In short, NLP reads the text or data, NLU makes sense of what is read, and NLG writes an appropriate response. NLP and NLU present ideas based on data and NLG determines how to transform these into language and determines how they will be communicated.

NLP, NLU, and NLG in Business

Machine learning and AI are becoming more a part of the online tools we use regularly, and it seems that this trend is bound to continue as the technology becomes more sophisticated. Compared to past years, access to data has improved and computing power has increased, allowing for the practical use of AI in fields such as marketing, eCommerce, media, and finance, to name a few. Below are a few business use cases where AI shows promise.

Sentiment Analysis

Social media sentiment analysis for listening is one of the best use cases of NLP. By monitoring what users or customers say about you on social channels, you can determine how to market your product or what new features to introduce in the future.

Spell Checking and Writing Enhancement

Mobile devices now have the ability to predict what you're about to type and autocorrect what you've already typed-all thanks to developments in AI and language processing. Certain companies like Grammarly and Ginger have also leveraged this technology to provide writing assistant services to individuals and organizations.

Entity Recognition

AI can help systems recognize certain entities in text or speech, including places, individuals, and certain products or objects; for example, when providing feedback online, entity recognition can determine what product you're providing feedback about, what type of feedback you're providing, and a user's location. This information can then be used to enhance customer service and the overall customer experience for eCommerce websites.

NLP, NLU, and NLG combined in a seamless system can help businesses get key insights about customers, competitors, and the market. It's a tool that has been leveraged by different companies for a variety of business use cases, and it has proven beneficial in understanding and predicting customers' behavior and decision-making process.

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