As artificial intelligence progresses and technology becomes more sophisticated, we expect existing concepts to embrace this change — or change themselves. Similarly, in the domain of computer-aided processing of natural languages, shall the concept of natural language processing give way to natural language understanding? Or is the relation between the two concepts subtler and more complicated that merely linear progressing of a technology?

In this post we’ll scrutinize over the concepts of NLP and NLU and their niches in the AI-related technology.

Importantly, though sometimes used interchangeably, they are actually two different concepts that have some overlap. First of all, they both deal with the relationship between a natural language and artificial intelligence. They both attempt to make sense of unstructured data, like language, as opposed to structured data like statistics, actions, etc. However, NLP and NLU are opposites of a lot of other data mining techniques.

Natural Language Processing

NLP is an already well-established, decades-old field operating at the cross-section of computer science, artificial intelligence, an increasingly data mining. The ultimate of NLP is to read, decipher, understand, and make sense of the human languages by machines, taking certain tasks off the humans and allowing for a machine to handle them instead. Common real-world examples of such tasks are online chatbots, text summarizers, auto-generated keyword tabs, as well as tools analyzing the sentiment of a given text.

What NLP does

NLP in its broadest sense can refer to a wide range of tools, such as speech recognition, natural language recognition, and natural language generation. Yet, the most common tasks of NLP are historically:

tokenization;

parsing;

information extraction;

similarity;

speech recognition

natural language and speech generations and many others.

In real life, NLP is used for text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, relationship extraction, stemming, text mining, machine translation, and automated question answering, ontology population, language modeling and all language-related tasks we can think of.

NLP Techniques

The two pillars of NLP are syntactic analysis and semantic analysis.