Top Three Applications of Natural Language Processing

A Tractica report estimates NLP market to grow to $22.3 billion by 2025:

NLP Statistics Chart 2017-2025

Computers use natural language processing to interact with written and spoken text. This allows them to decipher the meaning and respond in a way that makes sense to the user. NLP makes data science more accessible to the general public and enables us to leverage data more efficiently and intelligently.

Processes like machine translation, deep learning, and replicating the patterns of human language play an integral part in the modern world.

Language is the tool humans use to understand the world around us. Written and spoken words define how we perceive reality. They also shape the way we view ourselves and other people. Data science continues to develop at a phenomenal rate. Artificial intelligence can process language in higher volumes than the human brain.

Data scientists are still working on enhancing natural language processing and making it closer to human abilities. Nonetheless, businesses are already using it in many real-world situations. From cybersecurity to customer service, artificial intelligence is changing industries across the globe. Here are three examples of how NLP is changing the way we live our lives today.

1. Natural Language Processing in Recruitment

CV writing is a skill in itself. The main thing that defines a good CV is language. The rules and conventions can vary depending on the industry you're writing for, but the basic principle is the same. Employers need to get an idea of who you are and what you're capable of.

Processing several candidates from social media sites can be time- consuming. With the help of natural language processing, this doesn't have to be the case. Recruitment consultants can begin shortlisting candidates before they meet in person. The recruitment world moves fast. Reading every document would be impossible. This means professionals who work in this field need a little help.

One of the main applications of natural language processing in recruitment is to extract key information without reading the text. Artificial intelligence identifies details such as qualifications, location, and skillsets by using a process called information extraction. Using tags that relate to the different parts of speech (nouns, verbs, adjectives, and so on) it is possible to identify keywords and phrases in a CV or covering letter.

As a result, laborious and time-consuming tasks such as compiling a list of potential candidates who meet the basic requirements for a position are streamlined by artificial intelligence, as opposed to a team of people. AI-powered ATS speeds up the recruitment process, but it also provides employers with a way of eliminating any candidates who are not suitable without the need for an interview.

NLP applications in Recruitment ATS

Ultimately, natural language processing has revolutionized the way recruitment companies do business and also reduced the amount of money they need to spend on staff. It also benefits employers in general. Although artificial intelligence is not perfect, machine learning techniques have helped to connect companies with candidates who have the most relevant skills and experience.

2. Applications of Natural Language Processing in Social Media

For most people, social media is now an integral part of life. Whether you use Facebook to catch up with friends and discover new content or whether you are a business who uses the platform to gather information about your potential customers and clients, it has become an essential part of daily living. Natural language processing is central to the way social media works.

Sentiment analysis allows businesses and individuals to understand how an audience feels about a piece of content, an event or status update by analyzing their reactions to it. Take a look at this Twitter sentiment analysis of the US airlines that shows how customers perceive each of the airlines’ responses to a major weather event.

Twitter Sentiment Analysis Airline

Machine learning can detect linguistic characteristics that relate to the way people feel. For example, Python can pick up on positive, negative, and neutral words as well as certain patterns of written language. As a result, we can get an overview of the way people are interacting with an article, video, blog post, or image.

Though data scientists have made progress with this technique, artificial intelligence can still struggle to process human language accurately. Passages of text that include elements such as irony, hyperbole, or phrases that express two opinions simultaneously can often be misinterpreted. Unfortunately, complex concepts such as detecting multiplicity and understanding context still present a significant challenge to data scientists. Programs that process NLP don’t have the ability for critical thought. Consequently, the data that’s gathered usually needs to be interpreted by humans before it can be of any real value.

3. Applications of Natural Language Processing in Customer Service

Natural language processing, deep learning, or machine translation can be difficult to understand. Especially for those who are not technically minded. Though many people may not realize is, this technology is frequently used in every day, real-world situations.

This technology is something that companies have already integrated into their working practices. A recent example is the chatbots. These simple but effective tools have been embraced by practically every forward-thinking digital company. Some of the most common uses of chatbots include:

  1. Answering Frequently Asked Questions
  2. Providing Out of Hours Customer Support
  3. Gathering Data and Customer Information
  4. Diagnosing Problems and Directing Enquiries

Practical applications of natural language processing like this demonstrate how artificial intelligence and machine learning can potentially improve the overall customer experience. Businesses can offer an immediate response to the most commonly posed queries. This creates the impression of 24/7 service, even if their staff only work standard business hours. Here is an example of how Mastercard used chatbots to serve consumers over Facebook messenger. NLP component is processing the situation in its entirety instead of taking customer’s verbatim input:

Applications of NLP and Chatbots in Mastercard Customer Service

Natural language processing is still in the developmental stages within this sector, and there are still a lot of things that it can’t do. Complex cases involving complaints or questions that aren't integrated into a database of FAQs will produce an inaccurate or irrelevant response. This problem can be addressed by including a link to an email account, manned live chat service, or phone number.

Essentially, natural language processing in customer service seeks to improve and refine the overall experience by freeing up staff time and allowing customers to access information quickly, without the need for long waiting times or unnecessary back and forth conversations.

Final Thoughts on Natural Language Processing Applications

Applications of natural language processing seek to make our lives easier and more efficient.

Data scientists are attempting to improve the world around us by programming machines to understand and respond to real human language. To do this in a way that mirrors an organic, human reaction presents several challenges. Progress is being made, though. In practical terms, artificial intelligence focused on processing NLP can be used as a tool to help businesses save time and money. It also allows data science to enrich the value of data gathered from social media and other online sources.

The progress that has been made in the world of natural language processing is already significant. As artificial intelligence continues to improve, this will continue to be the case. Developing machine learning to understand human language will allow us to make revolutionary changes in many aspects of our daily lives. Though there is still a lot of work to be done, especially in areas like deciphering nuance and subtext, this technology is already having a positive impact on our daily lives.

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