In this article we will cover a working definition of natural language processing – an NLP definition.
You’ll learn what NLP is, why it matters, and how it is affecting the business landscape.
A Natural Language Processing (NLP) Definition
Natural language processing, or NLP, is a field with an artificial intelligence that focuses on processing human language.
This means that NLP-powered programs can:
- Analyze text and speech
- Understand the meaning of text
- Generate new text from scratch
NLP-based applications can generate significant value both for businesses and for consumers, and this technology will likely revolutionize the way that we interact with computers.
For instance, NLP powers certain applications such as voice user interfaces and voice search. Today, voice search may only be a small percentage of the overall search volume, but that is changing rapidly.
Here are a few examples that demonstrate this trend:
- Shopping done using voice activated devices will grow from $2 billion in 2017 to $40 billion in 2022, per Statista
- The voice user interface industry is set to grow from $13.65 billion in 2020 to $95.41 billion in 2030, according to Allied Market Research
- 57% of those between 25-49 years old use voice technology at least once a day, according to PwC
These are only a few of the many trends that demonstrate why voice-powered technology, which is in turn powered by NLP, will become the norm in several years. Also, as many research reports have shown, it is already normal for certain segments of the population.
Voice user interfaces and voice search, however, are only two examples of NLP-based applications.
- Chatbots. Chatbots are text-based interfaces that can be used in many areas. Amazon uses them for customer service. Other companies use them for technical support. And companies such as WalkMe use them to provide software training and support.
- Text-based search. Search has become a critical feature for many products in today’s world. Google certainly is one example, but Amazon, LinkedIn, Facebook, Instagram, and many other companies use search. Although early search engines did not use NLP, NLP-based features have become introduced over time. Semantic search, for instance, attempts to understand the underlying meaning of and predict the user’s intent when they search.
- Sentiment analysis tools. Sentiment analysis refers to the analysis of emotions within text. In the context of NLP, this typically is scored on a scale that measures positive to negative. Software such as brand monitoring tools can be useful for understanding how people feel about a particular topic, such as a product, an event, or a PR communication.
- Data mining. Data mining refers to the extraction of information from large data sets. Through textual analysis, NLP-powered applications can sift through large sets of resumes, legal documents, medical documents, and other text documents. This can save an enormous amount of time when it comes to job tasks such as research.
In short, NLP is used to automate many functions of business that are otherwise performed by humans.
How NLP Will Impact the Business World
To understand how NLP will affect the workplace and the business landscape, it is useful to look at automation in general.
Automation is the use of software to perform tasks that are normally done by humans.
In the case of NLP, as mentioned, this means performing tasks such as reading, writing, listening, and creating text.
Jobs that will be impacted by NLP-powered automation include:
- Customer service agents
- Technical support staff
- Administrative and clerical staff
In fact, over time, even jobs that require face-to-face interaction may be facing competition from NLP-powered tools. Robots, for example, are already being used in restaurants, hospitals, and health care settings. As these robots become more common and as NLP becomes more sophisticated, other job categories will feel the impact of NLP-driven automation, such as jobs in retail, hospitality workers, and so on.
These trends will certainly have a negative impact on low-skilled jobs, at least in the short-term. According to some predictions, for instance, as much as 50% of low-skilled jobs are capable of being automated.
It is also worth pointing out, however, that the trend towards automation will drive the creation of new types of jobs.
Just as low-skilled jobs decrease, certain job categories, such as those in computer science and mathematics, will become more in-demand.
One major result is the need for employees to upskill and reskill.
Many employees already feel the pressure to increase their skills and this is perhaps one reason why online education companies such as Udemy and Udacity have become so popular.
At the same time, organizational restructuring has also become a common approach to dealing with disruptive innovation. Businesses are adopting new processes, practices, software, and strategies.
In-house digital adoption programs, for instance, are designed to streamline the adoption and implementation of new software through structured software onboarding, training, and support. Programs such as these not only support employees’ training needs, they also enable organizations to innovate and stay agile.
Ultimately, NLP and other automation technologies will reshape every aspect of our lives, both within the organization and in our daily lives. Anyone interested in preparing for the digital future should keep a close eye on these technologies and how they are reshaping the business landscape.