Natural Language Processing: Use Cases & Challenges

Natural Language Processing (NLP) uses AI to understand and communicate with humans in a way that seems natural. Machines are able to understand human language in a similar way to us. NLP is at the heart of Siri, Alexa and any common chatbots you may be using. Let’s take a deeper look at what exactly it is, and how it works.

What Is Natural Language Processing?

NLP is a branch of AI that bridges gaps between humans and machines. Computers can be taught to make sense of written or spoken language, involving teaching computers how to understand the nuances of language. These include grammar rules, semantics, context and even emotions.

NLP facilitates machine learning from vast amounts of data. This is how they can come to understand and analyze text or speech inputs in real time- based on the past data they studied.

Why Use NLP?

Well, with the power of NLP applications can be made such as:

Plain text or speech within NLP becomes accessible. This means that social media posts can be understood, and any other comments or engagements from customers can have value for your business. 

NLP has revolutionized the way we interact in the virtual sphere. In short, it has bridged the gap between humans and machines.

Examples of NLP

We all use NLP in our daily lives, whether we are aware of it or not. All virtual assistants such as Siri and Alexa work via NLP. Another main use case of NLP is through sentiment analysis, where machines comprehend data and text to determine the mood of the sentiment expressed.

For example, NLP on social media platforms can be used to understand the general public reactions towards events. If a post is created, NLP can understand if people are supportive, unsupportive, indifferent or any other kind of emotion- as a result of comments left. 

Google translate also uses NLP through understanding sentences in one language and translating them accurately, rather than just literally, into another. This is because words and phrases between languages are not literal translations of each other. NLP helps Google translate to achieve this goal including grammar and semantic meaning considerations.

Challenges Of NLP

As with all areas of the eCommerce world, as technology advances we continue to see growth in AI fields. NLP is no exception. It is a field that combines linguistics, artificial intelligence and computer science to interact with human language. As far as it has come, it still has ways to go.

One major challenge facing NLP is the ambiguity of human language. Contextually, words’ meanings change. Depending on how the word is used in a sentence, the word has different meanings. For example: the word “bank” can mean a river bank, a money bank or to bank on something (rely on it). 

In order for a computer to fully understand the different meanings in different contexts, sophisticated algorithms need to be enabled. Surrounding words and phrases need to be analyzed, and this is not easy. 

Similarly, colloquialisms, slang and dialects all complicate things for the computer systems. Language is sophisticated and constantly evolving. It is not a static form, and in order for the NLP to keep up to date with trends, it has to be always learning and training.

There are also privacy concerns when it comes to sensitive information within text data. Although it may be useful to train the computer on these formats, is it ethical? Medical records and personal conversations may be across the line. NLP researchers have to grapple with these kinds of ethical issues.

Lastly, tonal elements are hard to pick up via the computer. Irony, sarcasm and excitement can be hard to understand if not already accounted for.


By now, Natural Language Processing is a huge part of our life and reality and there’s no way this will change. As technology advances, the capabilities of NLP will improve. For now there are still challenges to overcome, but the benefits of NLP in spite of these cannot be denied.