What Is Natural Language Processing


Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of language for machines. Considering the nuances of language, NLP is an interesting development in the field of AI. While tools such as content analytics platforms can understand the context of language, a computer is yet to “read between the lines” or intuit language the way a human does.

Technological advancements have changed the paradigm for doing business. As part of a digital transformation, businesses are exploring means to innovate and scale past the competition. The field of Artificial Intelligence (AI) is one arena that has seen a rapid acceleration in practical application. Nowadays, AI is no longer an abstract theory or a figment of science fiction but a tangible tool for businesses to gain a competitive advantage.

On a high level, NLP leverages computer programs to interpret unstructured content using AI and machine learning. This mechanism allows computers to understand the context of language much as human brains do. Advancements in AI neural networks have taught computers how to mimic the neural networks of human beings. In much the same way that a human being learns by continually being exposed to stimuli, a computer gets more efficient in interpreting language through exposure.

By structuring and interpreting unstructured information, computers generate insights from untapped data for businesses. This enables businesses across industries to get a deeper understanding of the public perception around consumer behavior, products, and brand equity. Decisions that were previously made on guesswork are now guided by data-driven insights. This is because machines are better, more accurate, and more consistent in interpreting language than humans. NLP processes data in real-time, 24/7, for consistent and accurate results. As a competitive advantage, NLP can be used to analyze large data sets such as social media monitoring, customer support, and online reviews.

Natural Language Processing Examples 

AI tools are most effective when they find an application in daily life. Here are some examples of the way NLP has improved daily life across industries:

NLP and Healthcare

 In healthcare, NLP tools can predict diseases based on electronic health records and the patterns of a patient’s speech. This innovation has been used to diagnose health conditions in areas such as mental health and cardiovascular health. As healthcare moves to telehealth, NLP can be used to help in service delivery.

NLP and Sentiment Analysis

 Businesses can listen on social media to what customers are saying by using NLP. Using this technique provides businesses with a lot of insights into customer’s choices and decision-making processes. Access to previously unmanageable large sets of unstructured data gives businesses access to data they did not even know they had on social media.

NLP and Email

Tech giants such as Google and Yahoo use NLP to filter and classify emails by instantly analyzing incoming text. This enables tech companies to filter and stop spam before it enters a user’s inbox.

NLP and Home Assistants

Apple’s Siri and Amazon’s Alexa are great examples of voice driven interfaces that use NLP to make everyday life better for humans. From turning off the lights at ome to playing favorite songs, these home assistants use voice recognition to help humans.

A document-heavy industry, legal is getting help from NLP to automate routine litigation tasks. It can help legal teams be more efficient and save money by having a better strategic focus.

What Industries Use NLP

Most industries will be disrupted by the availability of NLP technology including legal. While lawyers are not going to be replaced by technology, the way lawyers do their job will be changed. NLP automates the time-consuming, redundant tasks that slow down lawyers. This technology will save time and reduce errors by extracting meaning from content and

collecting data and documents instantaneously. Lawyers work on billable hours and NLP technology can save hours of labor allowing legal professionals to focus on cases and clients that matter the most. In addition, NLP may make legal support more affordable to a larger population of people.

Financial Services

More than other industries, the financial services industry is time sensitive. News around the community spreads quickly and can influence an investor’s decision-making. For an investor, filtering relevant information from the 24/7 news cycle is laborious and time-consuming. NLP technology can help make smart financial decisions by pre-filtering steps automatically. Also, financial services can use NLP technology to increase compliance in large amounts of paperwork eliminating mundane and repetitive steps in the process.


Since the COVID-19 pandemic, the healthcare industry has had a significant paradigm shift. With the prevalence of telehealth, efficient coordination of medical data is a top priority. NLP technology can be used to analyze doctor’s records and find hidden correlations between diagnoses, symptoms, medications, and treatment. These new insights give healthcare professionals the ability to manage quality care initiatives and better predict treatment outcomes for patients. /

The Two Subfields Of Natural Language Processing (Understanding & Generation)

Given the high-level definition of NLP and the extent of its capability, it is no wonder that NLP has many subfields. More specifically, there is Natural Language Understanding (NLU) and Natural Language Generation (NLG).

So, what is the difference? NLU is more focused on the process of reading and interpreting language such as giving a command to Apple’s Siri. NLG is geared towards the process of generating a natural language. For example, after asking Siri a question about the weather in Portland, OR the computer would use NLG to respond with “rainy with a chance of thunderstorms”.

To further understand the subsets of NLP, let’s put them in the context of the eCommerce industry. The ecommerce industry is focused on the virtual customer experience and interactions. NLU would drive site search in eCommerce. It is activated when users have a search query, they expect to be understood and to get an appropriate search result based on the query. In eCommerce, NLG would be used to power chatbots that generate language. Chatbots speak back to the users and facilitate an interactive and informative user experience for the customer. Together, NLG and NLU make eCommerce more powerful and responsive to user’s needs and preferences. This in turn drives customer loyalty and increases revenue.

NLP, what is the future?

Given the capabilities of NLP in select industries and the increased reliance on technological advances across the business sector, the future for NLP looks bright. Perhaps, what is most surprising to the average person is how NLP technology has seeped into and improved everyday aspects of life. By touching all areas of life and improving daily interactions, NLP can have a positive effect on the individual’s quality of life as well as society.

Foremost, NLP technology makes communication easier and more efficient. This ensures that every interaction we have is in alignment with our goals and has a positive outcome. NLP also influences mental health and helps individuals overcome personal obstacles and psychological barriers. Because NLP mimics the human brain, it augments tools and techniques inherent to us without us being aware of its psychological impact.

The human brain is prone to error and inconsistency. By relying on machine cognition, everything becomes a lot faster and more automated. The adoption of NLP technology has allowed major organizations to become more agile and flexible with decision-making and strategic initiatives. For businesses, access to unstructured data, data they did not even know they had provided new insights. The intelligence gathered from the data by NLP-powered machines changed the way businesses make decisions.

The prediction is that in the future NLP will become more mainstream. Such NLP practices as text summarization, NLP consulting, and text mining will become more sophisticated with new developments.NLP technology will not only influence select industries but empower decision-making across global markets.


How will humans respond to a major integration of NLP?

Much like with the adoption of AI, the proliferation of NLP in society has been met with enthusiasm rather than disdain. Humans see the potential of technological advancements to make work and life easier. Humans becoming more comfortable with advanced technology can be attributed to the widespread adoption of smartphones and the increased demand for elevated customer service. Nowadays, humans see the power of automating the mundane and repetitive tasks in life so they can focus on higher value-added activities requiring human intelligence, ingenuity, and creativity.

In the future, NLP technology will become an integral part of life. As AI continues to become more advanced, the next level of progress for these technologies to be able to “read between the lines” for human language. To become as sensitive and nuanced to the context of human language as the human brain. This will cause the AI and NLP robots to become more sophisticated and useful in the workplace. As these technological capabilities are becoming more mainstream, they are ushering in a new era of work.

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