Using NLP For Business Success
Many companies are adopting Natural Language Processing (NLP) because of the great business and growth opportunities it brings.
In this blog, we explore this amazing AI-powered technology and how it can help your organization. You can also use the Table of Contents below to skip ahead to any topic you are interested in.
- What is Natural Language Processing?
- Categories of NLP technology
- Why is NLP so important?
- What’s the difference between NLP and Text Analytics?
- Machine Learning (ML) and NLP
- Knowledge Graphs and Neural Networks
- What are examples of NLP applications in business?
What is Natural Language Processing?
Natural Language Processing (NLP) is an artificial intelligence (AI) technology that allows a machine to recognize and decipher the nuances of human language. It organizes unstructured data by analyzing it for relevancy, differences in spellings, correlation, and semantic meaning. It tries to understand different lexicons, grammatical syntaxes, and the relation between words and phrases, just as a human does. And remembers it.
NLP is used successfully today in speech pattern recognition, weather forecasting, healthcare applications, and classifying handwritten documents. There are in fact so many NLP applications in business we ourselves use daily that we don’t even realise how ubiquitous the technology really is. Smart assistants like Siri and Alexa, our car navigation system that tells us the fastest route, our favourite OTT streaming channel that suggests which movies we’d like to watch, autocomplete predictive texts on our phones, translation apps - they are all examples of how NLP has become an integral part of our lives.
Categories of NLP technology
There are 3 basic categories of NLP that are used in diverse business applications.
Natural Language Understanding (NLU)
Natural Language Understanding (NLU) enables unstructured data to be restructured in such a way that a machine understands it, and then analyzes it for meaning. By applying deep learning, it can categorize information even more granularly, from a massive collection of data. It can discover key facts, and even deduce characteristics of the entity or features (organization, artist, author, politician, etc) it finds.
Natural Language Generation (NLG)
NLG analyzes thousands of documents and generates descriptions, summaries and explanations as input data for an AI/ML model. It analyzes and generates both audio and textual data.
Language Processing & OCR
NLP algorithms detect and process multiple languages for translations. When data is in a video format or as scanned documents, these same algorithms can be combined with Optical Character Recognition (OCR) technology to convert this data into plain text that can be searched.
Why is NLP so important?
The interest companies are showing in embracing NLP-based solutions is gaining momentum fast. According to an industry report, the forecasted global NLP market size is set to be US$ 35.1 Billion by 2026. The rise is in almost all verticals including healthcare, credit card and insurance fraud investigations, and text analytics for customer sentiment analysis. NLP is also generating a great deal of interest in intelligent document analysis in aviation, drone control, robotics, and heavy machinery industries. Companies are realizing that AI-powered solutions are only going to get bigger and better. And if you don’t explore the technology now, doesn’t mean your competitors won’t.
The key to understanding how NLP can be applied to your business, and how it can help your growth, is to understand its basics. There are many subsets of technologies related to NLP and many a time they are confused with one another. The most common is thinking that NLP and text analysis are the same. They are not; rather there is a significant distinction between the two.
What’s the difference between NLP and Text Analytics?
NLP technology understands, interprets, and classifies a company’s raw, unstructured big data collected from different sources like customer reviews, social media listening, employee forums, etc. Text analytics takes this now organized data, and drives it through machine learning (ML) algorithms to gain insights from it. This is how text analytics helps a company discover business intelligence for prescriptive and predictive analytics within minutes.
But before an ML model can begin work on a set of data for your industry, and you in particular, it has to be trained. And in order to be trained, it needs to have an annotated corpus of data that is representative of the text that will be eventually analyzed. Without NLP, there is precious little that can be done to train the machine model.
NLP is often accomplished using neural networks to identify hidden patterns and correlations in unstructured data. Once discovered, these patterns often yield key semantic insights. For example, Named Entity Recognition (NER) can be used to examine textual data and identify any person, place, location, brand or business. In the context of business intelligence, this could be used to track and monitor conversations about competitors.
We will look at neural networks and knowledge graphs a little more in detail later. But first, let’s examine why ML and NLP are so closely connected.
Machine Learning (ML) and NLP
Machine Learning (ML) is a form of AI. It is a technique used to automatically identify patterns in data that can be used to provide actionable insights that a business can use. This data may be from social media websites, videos, chatbots, customer surveys and reviews, electronic health records (EMRs), Voice of Employee (VoE) programs, or numerous other sources. Natural Language Processing transforms all of this human language, with all its intricasies, into a format that a machine model can understand. It bridges the gap between the machine and a human being.
Close to one exabyte of data is created daily on the internet through news, websites, blogs, emails, reviews, videos, forums, e-commerce, chats, and a hundred different other ways. We use the internet to access news or ask a query almost everyday. We are curious to know more about a certain politician, sportsperson, an event, or even a holiday spot. We go to TikTok and search for a new recipe. We search for customer reviews on Amazon before we hit the checkout button. We go to YouTube because we want to know how a certain product works, or how to play a tune on an instrument. We search for something but it’s in a different language, so we hit Google Translate. (Actually - around 200 million people use Google Translate everyday, which accounts for one billion translations on a daily basis)
This huge deluge of big data being created continually can be a boon for companies because it’s all just waiting to be discovered and harnessed for business intelligence. And companies are already doing it. In a corporate set-up too, 80% of data is just unstructured. It is across documents, emails, office intranets, chatbots, attachments, videos, audio, webinars, presentations, and a myriad other formats. This is another major reason why the NLP market is growing exponentially.
None of these ML-based applications we take for granted are possible without NLP. It is unimaginable to think about categorizing and annotating all of this data manually, with the same speed and accuracy that an AI-led computer program would. NLP accomplishes the task not only fast, but also effectively and efficiently. And as the ML algorithms improve through each task, and as they access the data and use it, they begin learning the patterns by themselves. The richer the data, the more effective the results. And human intervention is no longer required.
Knowledge Graphs and Neural Networks
Now that we’ve taken a look at how NLP helps drive an AI/ML model for business intelligence - let’s understand how NLP actually achieves this magnificent feat.
2 magic words - Neural Networks and Knowledge Graphs
Artificial Neural Networks or simply “Neural Networks” (NN) are types of mathematical algorithms that are modelled loosely on how the human brain works to store information. A neural network is used to pre-train an ML task through word embedding, in order to teach the algorithm how to predict a word based on its context. This is how text classification is done in Natural Language Processing.
NNs are still under significant R&D by data scientists, and are still a long way from the remarkable superiority of human brain function. However, they can still provide accurate insights in many areas that require predictive analysis and control, such as stock price movements, geo-spatial mapping, and signal filtering.
With deep-learning technology, neural networks identify the words or phrases as named entities, disambiguate them based on surrounding context, and then categorise them. For example, a neural network will help you know when someone is talking about Paris, the city, versus, Paris, the socialite, gathering the context from a mention of the Mona Lisa or any other entity/feature related to the city.
A Knowledge Graph contains millions of facts and data points about every notable person, place, business or product. All of it is used to provide even more context to the already existing information. Armed with this enriched metadata, the knowledge graph shows which entities are related to one another, and makes content suggestions and comparisons faster and more accurately.
Let’s take the above mentioned example of Paris. When a NN identifies the context and focuses on Paris, the city in France, it connects all the information it has clustered together and classified it as related to Paris. For example, it will know that the Eiffel Tower is located in Paris. The Eiffel tower is a tourist attraction, and so is the world-famous Louvre. So it connects the two as related to each other. It will then form a connection to the Mona Lisa, since it’s the most famous of the Louvre’s collections. It will then link the Mona Lisa to the artist Leonardo da Vinci, and by that connection realise that it’s the most important painting from the Italian Renaissance. Thus connecting Paris, the city, to Italy, the country in Europe.
It is for this reason that NNs need to be updated regularly so that the data remains current. Doing so ensures that the coverage of the knowledge graph remains comprehensive and consistently high. And in turn, the results given by the ML model are most relevant and accurate. This also enables semantic search to provide you an even more expressive and powerful search experience that can help you navigate deeper into your data.
Knowledge Graph that shows the relationship between different entities
What are examples of NLP applications in business?
Let’s deep dive into some examples of modern business applications of NLP and see how the technology has transformed these industries and their operations. These include aircraft maintenance, drone & UCV control, automated trading, predictive text, medical and healthcare, sentiment analysis and many more.
Aircraft maintenance is a very important aspect of the aviation industry. Almost 60% of repair and overhaul is in fact conducted for civil aviation. Mechanics in aerospace, and also defense, use NLP to glean information about specific issues from extensive aircraft manuals. These aircraft maintenance manuals can sometimes be in different languages as well. Through Semantic Search, and Named Entity Recognition, mechanics can search and understand the notes from pilots or other individuals describing the problems they faced mid-flight.
These notes can be in audio format or handwritten. For handwritten formats, NLP uses neural networks for handwriting recognition and provides the information that the user is looking for. AI has revolutionized data diagnostics so much that Airbus thinks AI/ML enabled control engineering (fault detection, isolation, and recovery) can address unscheduled aircraft grounding almost completely by 2025.
In the finance industry, NLP applications in business can be seen in the use of automated trading. This is when you use a computer program to place a trade on your behalf. A person can define instructions such as time, price, and volume into the program, and when the share price corresponds to the value defined, the program executes its directions and makes the transaction. NLP can also review news on financial results, mergers and acquisitions, and make recommendations on which stocks would be a good investment, based on this data.
Automated Phone Systems
When a customer service number at a bank or ticketing system puts you on an automated phone system, that’s because of NLP. It’s also because of NLP that we have computer-generated languages that sound just like a human voice. The technology is based on predefined system rules and uses speech recognition and interactive voice response (IVR) when it interacts with you. That’s how it guides you through a transaction, and also gives you the option of choosing the language you prefer. More and more companies are choosing to use NLP for customer interaction as it is more convenient, cost effective, and predictable.
Drone and UAV Control System
The aerospace industry uses NLP technology to control drones and unmanned aerial vehicles (UAV). They use it for real-time path planning, adaptive control, and obstacle recognition. It is also applied in self-governing planning systems that use speech recognition and air traffic control language as a base. There is significant research being conducted in this area through high-fidelity simulation, especially for adaptive control for uncertain environments.
Insurance & Credit Card Fraud Protection
NLP business applications have helped in financial fraud protection tremendously. Insurance and credit card companies are always alert when it comes to fraudulent transactions. These companies, especially in the insurance sector, have huge KYC (know your customer) records, location-based information, social media information, and user-sentiment big data. They use NLP technologies to analyze all this data for text and sentiment analysis.
Text analytics also processes the patterns from a massive chunk of textual data from insurance applications. It checks for similar claims, similar circumstances, and creates a knowledge database. This is how it detects organized fraud by linking common keywords or similar accidents, even if they are in different locations, and by different claimants. This information not only helps them dismiss bogus claims, but also process genuine claims faster, and improve customer satisfaction.
Patient Voice & Healthcare
Hospitals and healthcare providers are using NLP technology more frequently now than before, to capture and manage patient notes, and electronic health records (EHRs). Patient feedback, their waiting room experience, post-surgery care, opinions and feelings, are all analyzed through AI/ML models using textual data from in-clinic questionnaires, post-appointment surveys, and feedback web forms. This helps them evaluate the quality of their service and offer a more enhanced patient experience.
When you want to reply to an email, and let your smartphone finish your sentence for you with phrases like, “Thanks for letting me know”, or “How are you?” - that’s NLP. And when you type a word and it gets autocorrected, much to your chagrin sometimes, that too, is NLP. Autocomplete and predictive text are both helped by NNs that cluster words together as they try to understand the semantic meaning of what you’re trying to say. And as a result, help you complete your sentence.
NLP enables voice recognition algorithms to recognize words and speech patterns, and infer meaning from them. You can say the word and get your car to phone someone, or you can ask Alexa to play you your favorite song. Smart assistants are in a way, how movies used to show AI would be - a machine and a human talking to each other. But it’s so uncomplicated that we don’t even think of it as state-of-the-art AI. Our smartphones and smart appliances give us useful responses when we talk to them in a conversational style; they understand what we’re saying despite our dialects and the tonality of our voice - all because of NLP.
Spam filters are one of the first applications of NLP in business. Words and phrases are identified as spam, and in categories such as social, promotion, or primary. If you are a Gmail user, you must have seen this categorization in your inbox. So you’ve got NLP to thank for making your inbox manageable by sorting your emails based on relevancy. In many companies, spam filters also check for ip addresses, and automatically send tagged emails to the junk folder.
Natural Language Processing has made search engines smart, and especially helpful to e-commerce websites. With neural networks, the NLP technology enables search engines to understand the query even before its completed. And when you’re given results, the engine also gives you additional, similarly relevant results, in case you want more options. To understand the scope of efficiency and scope of the technology, we just have to look at the fact that eBay alone has 185 million users, and they account for 250 million searches daily.
Social Media Sentiment Analysis
NLP for social media listening is unique because it understands internet short forms (LOL, BRB, TL;DR), slangs, code-switching, emoticons and emojis, and hashtags. No matter what your customers choose to speak, NLP allows you to extract information from it, and prepare it for an ML model to ingest. Sentiment analysis further helps you analyze how your brand is doing based on positive, negative or neutral emotions it finds in your social mentions. And in this way it gives you actionable insights you can use. You can reach out to an influencer as part of your marketing strategy, alter your advertising campaign, improve aspects of your product or service, upscale your brand reputation, all based on public sentiment derived from social media monitoring.
There is information and knowledge to be found in many sources, but not everyone can be fluent in multiple languages. That’s why online translations are a boon for the most part, especially for researchers. And we wouldn’t be able to enjoy the many foriegn films and documentaries with subtitles on our video streaming channels, without the NLP technology providing speech to text translations at scale, so quickly and efficiently. Languages are so beautiful, so unique, and intricate that linguists are heavily invested in their morphology, anthropological linguistics, philology, syntax and phonology. They discover new insights continually, and these insights help data scientists improve AI/ML models for language translations.
In NLP, there is a task called Sentence Boundary Detection (SBD) that understands the boundaries of a set of words. It is one of its most fundamental tasks in translation. That’s why you can translate entire texts in different languages, and they match sentence by sentence. Google Translation alone is used by 500 million people to translate documents or text in 100 different languages. There are numerous other translation apps and websites.
However, because human language is so complex, machine translation still has a long way to go. Especially in languages that do not use spaces to mark the end of a word, as in Japanese or Thai. Word boundary detection is an area that data scientists are still trying to perfect.
Companies use text analytics to gain insights from any and every source of information that is related to them. This flood of data can be from news, social media reviews, tweets, online surveys, voice-to-text notes, or any other source. NLP converts this raw data into meaningful documentation that can be analyzed by a machine learning algorithm. Semantic Search further helps in understanding the meaning and intent behind words and phrases. Coupled with NER, text analytics matches a sentiment to an entity and by doing so lets you know how a third-party feels about you. This information can provide actionable insights that you can use for intelligent business decisions.
Optical Character Recognition
Raw data that is collected for text analytics can be from emails, invoices, service agreements, research papers, human resource documents, purchase orders, and other textual formats. But it can also be sourced from video formats on various platforms like YouTube, Igtv, Facebook, TikTok or in images (think Instagram or Pinterest). Natural Language Processing algorithms use Optical Character Recognition (OCR) technology for images, as well as for Video Content Analysis, to understand this image-based data. The technology converts the scanned file into a text searchable file, and in this way helps the machine model read the data to derive insights.
NLP has radically changed how industries function. It can amplify your business, and help you move in the right direction. It gives you better operational efficiency, scalability, agility, and resource management. As more and more companies move towards AI-powered machine models, it is time to study the effectiveness of your own legacy models. By adopting cognitive technologies like NLP, you can be at the forefront of technological advancements that make you a market leader.
Contact us to know how NLP can help you.