Natural Language Processing (NLP) vs Natural Language Understanding (NLU): Explore the Differences
Most of us who are enthusiastic about enterprise search and big data are aware of the concept of natural language processing. Wikipedia defines natural language processing (NLP) as "Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.". To make it more simple, natural language processing is a form of artificial intelligence that enables machines and computers to understand and process human language. This technology is immensely effective in helping enterprises extract the deepest insights from the most complex and unstructured enterprise data.
When we delve a little deeper into the concept of natural language processing and allied concepts, things do get really interesting. The technology that powers the most advanced systems communicating successfully with human beings is definitely not as simple as it looks. Natural language processing, as we know it, takes place in two steps, namely natural language understanding (NLU) and natural language generation (NLG). So, natural language understanding is the very first step of the complex process of making machines understand the nuances of human language. The next and final step in the process is natural language generation (NLG), which is all about making the machines capable of generating information in the natural language or human language.
Interestingly, the concepts of natural language processing (NLP) and natural language understanding (NLU) are very often used interchangeably. However, there are differences between them even though there are overlaps too. Both NLP and NLU are both concepts that are all about how the natural language spoken by humans can help them interact with machines and devices.
NLU is more focused on the machine learning aspect and it has multiple applications, right from categorisation of texts to archiving of data in relevant categories. This step is important because unless and until the system or machine is capable of understanding the data and its purpose, it can never analyse the information and neither can it produce the output.
As the challenge of Big Data continues to grow, NLP will play an even bigger role in helping enterprises overcome this challenge. The new age enterprise search tool like 3RDi Search is what the organisations will need in order to take the user experience to the next level through a platform that is intelligent enough to interact with the users.