There is no doubt that Natural Language Processing (NLP) is in high demand in today’s job market.
According to Market Data Forecast, the global Natural Language Processing market size is predicted to reach USD 14.67 billion in 2024 and USD 38.04 billion by 2029, growing at a CAGR of 21% during the forecast period.
And with many topics under the Data Science Umbrella, it could be challenging to understand why learning NLP could help you advance your career.
So here are five reasons to help you understand why learning NLP is essential in 2024.
- NLP is the KEY component of LLMs and Generational AI. You have probably heard that data in text form has been widely used to train and develop all the new AI models. However, all of that text needs to be understood by machines, and this is where NLP makes the scene. NLP is the bridge that helps machines to understand and figure out human language.
- Without NLP functions like vectorization and tokenization, we wouldn’t have Bard, ChatGPT, and, in general, Generative AI. We need NLP to convert the data text into numerical data (in clever ways known as vectorization or word embedding) so machines can process and understand it.
- NLP is used in many applications, and big companies offer NLP-related services like tagging, summarization, and Named Entity Recognition (NER), among others. If you want an example, take a look at Datasaur and check their NLP-based solutions for Legal, Healthcare, financial, Media, and e-commerce.
- NLP is used to analyze and understand large amounts of text data. This data can be used to extract insights, identify trends, and make predictions. NLP is used in various applications, such as sentiment analysis, topic modeling, and information retrieval.
- NLP is essential for building effective communication between humans and machines. As AI becomes more integrated into our lives, the ability to understand and respond to human language will become increasingly important.
What industries are using NLP?
These are just some of the industries using NLP. I added a short description of how they use it to give you a better idea of how NLP provides innovative solutions.
Tech: Search engines, social media platforms, e-commerce companies, and software development utilize NLP for tasks like sentiment analysis, recommendation systems, and chatbot development.
Finance: NLP helps analyze financial documents, detect fraud, and improve customer service interactions in banking and investment firms.
Healthcare: NLP can be used for medical record analysis, drug discovery research, and building chatbots for patient support.
Retail: NLP empowers retailers to personalize marketing campaigns, analyze customer reviews, and improve product recommendations.
Manufacturing: NLP helps analyze product manuals, identify maintenance needs through text data, and automate customer support.
NLP in action
Want to see how NLP is used in a very cool (I’m a nerd!) way?
And want to have access to their repo in GitHub?
Well, I have one exciting example of how NLP is used by one of the biggest newspapers in the UK.
The main idea was to train a model to identify quotes immersed in their article’s text.
So, the team of Data Scientists at The Guardian used Spacy, one of the leading open-source libraries for advanced natural language processing using deep neural networks.
But first, why do they want to identify quotes?
Here is what they say about it:
Quotes are not only a vital piece of accurate reporting but can also bring a story to life…For instance, accurately attributed quotes can be used for tracking shifting opinions on the same subject over time or to explore those opinions as a function of identity, e.g. gender or race. Having a comprehensive set of quotes and their sources is thus a rich data asset that can be used to explore demographic and socioeconomic trends and shifts. – Source: The Guardian
Want more info? Read The Guardian’s publication about their use of NLP.
I think this is an exciting use of NLP that will have many future applications. The team at The Guardian explains that this project could lead to a user interface tool for discovering quotes, enabling journalists to surface previous quotes quickly and check them against current statements or to enrich their articles.
Check the Guardian’s GitHub to explore their repo: Journalism AI – Quotes extraction for modular journalism.
Have you ever considered learning NLP? Watch my lesson about how to teach yourself NLP 👇👇
Lina Marieth xx
Also, check the Spanish version here
Quote of the week
When I don’t try, the only possible outcome is it not happening – Lisa Olivera.