Top Three Applications of Natural Language Processing
A Tractica report estimates NLP market to grow to $22.3 billion by 2025.
Natural Language Processing (NLP) is among the hottest topics in the field of data science. Companies are putting tons of money into research in this field. Everyone is trying to understand Natural Language Processing and its applications to make a career around it. Every business out there wants to integrate it into their business somehow.
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.
Over the years, Artificial Intelligence has overdone its ability to make things happen in the world of technology. NLP is a component of AI in the field of linguistics that deals with interpretation and manipulation of human speech or text using software. It enables the computer to understand the natural way of human communication by combining machine learning, deep learning and statistical models.
Due to the vast availability of Big Data, Modified Algorithms and Powerful devices, NLP is a rapidly advancing technology. There are several ways to approach NLP, starting from statistical and machine learning to rule based and algorithmic approaches.
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.
It stands for Neuro-Linguistic Programming and is based on the idea that the way we think, feel and behave is linked to the way we use language and our neurological processes. This quality efficiently enables human-computer interaction and also allows for the analysis and formatting of large volumes of previously unused data. According to Statista, the NLP market is expected to grow 14 times in 2025 than it was in 2021. This means increasing from around 3 billion USD to 43 billion!
Because just in a few years’ time span, natural language processing has evolved into something so powerful and impactful, which no one could have imagined. To understand the power of natural language processing and its impact on our lives, we need to take a look at its applications.
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.
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.
Hiring the right people is crucial. But what is ‘right’ and what really ‘matters‘ remains entirely a human prerogative. The role of NLP in recruitment is about freeing time for meaningful human-to-human interactions. It revolves around streamlining processes, revealing valuable insights, and engaging participants. NLP protects from infobesity, inattention, allowing to transform a high-touch hiring process into an enjoyable virtual journey.
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.
The development of social media has revolutionized the amount and types of information available today to NLP researchers. Data available from social media such as Twitter, Facebook, YouTube, blogs, and discussion forums make it possible to find relations between demographic information, language use, and social interaction.
Using statistical and ML techniques, researchers can learn to identify demographic information, language, track trending topics, and predict disease spreading. For instance, with Google Flu Trends it recognizes deception in fake reviews from symptoms mentioned in a tweet or food-related illnesses.
Social media has changed the ways we can use big data. Product reviews can help to predict pricing trends and plan future advertising campaigns. Political forums help to predict success in elections.
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.
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.
Businesses use various NLP techniques to analyze social media posts to get an idea of what their customers are thinking of them. Also, it is used by companies to understand the problems faced by their customers. Even the government uses social media to identify national security threats by analyzing the comments and posts.
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:
- Answering Frequently Asked Questions
- Providing Out of Hours Customer Support
- Gathering Data and Customer Information
- 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:
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.
AI-enabled customer service is already making a positive impact at organizations. NLP tools are allowing companies to better engage with customers, better understand customer sentiment and help improve overall customer satisfaction. As a result, AI-powered bots will continue to show ROI and positive results for organizations of all sorts. While there's still a long way to go before machine learning and NLP have the same capabilities as humans, AI is fast becoming a tool that customer service teams can rely upon.
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.
Natural language processing can bring value to any business wanting to leverage unstructured data. The applications triggered by NLP models include sentiment analysis, summarization, machine translation, query answering and many more. While NLP is not yet independent enough to provide human-like experiences, the solutions that use NLP and ML techniques applied by humans significantly improve business processes and decision-making.
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