Using NLP to master your data projects internally 🎓


Starting from the basics: What is NLP?

Basically, NLP, which stands for “Natural Language Processing”, refers to computers’ ability to exploit resources that belong to the field of language either through text or audio content. NLP is highly involved in many industrial frameworks thus allowing us for instance to ask questions to voice assistants such as Siri or Alexa or to parse the Web seeking for relevant answers through Search Engines whose core algorithms mostly remain confidential.

By nature, NLP is thus at the crossroads of two main domains which are respectively Linguistics and Computer Science while being (rightly) associated to many other buzzwords such as AI (Artificial Intelligence), Big Data and Machine (Deep) Learning.

Ok but I am not a data scientist, why should I care?

Data science is deeply transforming the structure of enterprises of all industries, sizes and types, and nearly everyone internally is or will soon be affected by its progress. Through NLP, referred to as a sub-segment of Data Science, it is very easy to understand the impact it will have on your everyday job.

First, NLP is used in a wide range of mass applications allowing almost any of us to easily jump into Data Science with a minimal knowledge of programming. Learning and practicing programming and NLP is no longer left to experts since the Internet is full of high-quality step-by-step tutorials that can help you get into it at your own pace.

Here is a list of some sources you should know:

As said before, NLP is presenting many fields of application and thus, it contributes to its universality. Businesses have also spotted its huge potential and are already using it for various uses cases like fraud detection, document recognition, customer engagement through voice assistants or text correction. Today, NLP is even able to help companies better interact with their customers by predicting their next request or claims.

In spite of rapid progress, there is also so much more to come. One of the most suggestive illustration of what is left to be done in the field of NLP would be the example of chatbots. Most of you probably already experienced the disappointment and the frustration when we first “discussed” with the promising chatbots implemented by many companies. The gap between our expected answer and the one provided by those chatbots were such that some of us never reiterated the experience.

Despite remaining challenges, the benefits of NLP for your organization are endless and will undoubtedly contribute to assist you for all the repetitive tasks to improve your efficiency and focus more on better serving your customers, and ultimately be more competitive compared to your peers.

Being trained to the impact of NLP at all layers of your business will also contribute to accelerate the digital transformation of your organization while improving performances at all levels.

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