More than 80% of the data available in the digital landscape is unstructured data. What is unstructured data exactly?
The texts, images, and videos that cannot be represented in a graphical or tabular format (basically in any consistent form of structured data) make unstructured data. Now unstructured data wouldn’t be of any use for businesses If not analyzed and structured. Therefore, we need NLP (Natural Language Processing) to process, organize, and interpret this unstructured data.
Another fascinating reason to use the Natural Language Processing technology is to form communication between individuals and modern technologies such as Artificial Intelligence, Machine Learning, Robotics, etc.
Digital enterprises use Natural Language Processing applications to ensure that modern technologies can interpret every single unstructured data released in their domain. As a matter of fact, the worldwide revenue from the Natural Language processing market is projected to reach around 43 billion USD by 2025 as more organizations attempt to fill the gap between human and machine communication.
Since Natural language processing applications and software are growing exponentially, it’s high time we start incorporating them into our business. How about we start with the basics?
This article will act as a comprehensive guide for Natural Language Processing technology, its use cases, and real-time examples, so make sure not to skip anything important.
What is Natural Language Processing (NLP)?
Natural Language Processing is the type of AI that aids in enabling computers to process and interpret human language. In simple terms, the NLP technology gives the machines the ability to read, understand and derive meaning from any unstructured data. Ideally, the Natural Language Processing model manipulates speech and text through a computational base powered by various software.
Businesses can further extract hidden patterns and insights from unstructured data and make informed decisions backed by solid facts.
Today, AI and NLP are blooming, thanks to the considerable improvements in data accessibility and increase in computational power. This exhibits how enterprises from every domain, such as healthcare, finance, media, human resources, etc., rely on Natural Language Processing techniques.
You will understand more of NLP by discovering its real-world applications and examples.
Real-time examples of AI and NLP
While the terms Artificial Intelligence (AI) and Natural Language Processing (NLP) might conjure images of futuristic robots, there are already basic NLP examples used by organizations on a daily basis. Below are a few prominent real-time examples of Natural Language Processing technology:
You might already be familiar with Apple’s Siri and Amazon’s Alexa, specializing in recognizing speech via voice recognition features. These smart assistants further infer meaningful and solution-based responses to human speech, which is primarily unstructured data.
Modern customers now expect smart assistants to understand contextual clues and make certain activities more manageable, such as ordering items, answering personal queries, and even responding humorously. All of this is possible with the NLP-based models backed up by AI that helps smart assistants to decode human speech.
An example of this can be seen in Vyrb app, that Appinventiv developed for its client, Innovative Eyewear. Vyrb is a voice assistant app for social media that allows voice posting on platforms such as Twitter and Facebook using bluetooth glasses and other wearables. This is a classic example of how organizations can utilize NLP-based smart assistants for their modern business processes.
Features like autocorrect, predictive text, and autocomplete are extremely common in smartphones and other online spaces. Predictive texts are almost similar to search engines that predict and suggest upcoming words based on what you type and search frequently. The autocorrect feature even changes your words to make the entire statement sound more relevant to the other user. Meanwhile, the machine is also learning from you every time it suggests.
The longer you use AI-NLP based predictive texts feature, the more it will learn and customize itself according to your preferences.
Digital phone calls
You might have heard this commonly “this call may be recorded for the training purposes” and wonder what that entails. The recorded calls are used for the NLP systems to learn from the database and provide improved and personalized services in the future. The automated systems direct customer calls to chatbots or service representatives who respond to customer requests using these NLP databases. This is a common NLP practice followed by every business that consists of digital telecommunications and customer service.
For instance, Appiventiv developed an AI-bot chat assistant based on Natural Language Processing to integrate in web and mobile banking applications of a global bank. This helped the bank with resolving customer complaints in real time, taking quick action on stolen credit cards or any theft and enhancing customer service to its maximum potential.
One of the most initial and basic applications of Natural Language Processing technology is Email filters.
The Email filter feature started out with spam filters and uncovering certain phrases and words; however, this has been upgraded to the most prevalent application called Gmail classification. The system recognizes if the email belongs to one of the three categories (social, primary, or promotions) on the basis of the contents. If you use Gmail, keeping your inbox in a manageable size is essential. Email filters keep the relevancy of your mail intact which helps you respond quickly.
As more Business Intelligence vendors have started utilizing natural language interfaces to data visualization, the Natural Language Processing technology is integrated into data analysis workflow. One example is smarter visual encodings that provide the best visualization for the right task based on the semantics of the data. This opens more opportunities for people to explore your business data using Natural Language Processing statements and question fragments.
When you apply NLP to data, it not only enhances the level of accessibility but also lowers the barrier to analytics across organizations.
Many languages don’t allow for straight translation and have various orders for sentence structures, which translation services overlook. But Natural Language Processing doesn’t overlook any sentence structure. With NLP, online translators can translate any language or fragment accurately along with presenting grammatically correct results.
Moreover, the Natural Language Processing software and tools can also recognize the language based on the input text and automatically translate it.
Apart from the examples mentioned above, the Natural Language Processing technology also gives content producers the power to automate metadata and pursue convenient brand interactions. Just like the examples, the applications of NLP are even broad and powerful. Let’s take a detailed look at some of the significant business applications and use cases of Natural Language Processing.
Use cases of Natural Language Processing in Business Landscape
A major problem arises when enterprises have large chunks of customer data that do not provide any insights and information to regulate the business. Natural Language Processing applications and techniques help analyze irregular data to identify sentiments, feedbacks, patterns, and other business-related insights. What else can NLP be used for? Let’s find out.
One day you search for a product on Amazon, and for the other consecutive days, similar products are advertised through Google to you. Did you figure out what happened here?
Target Advertising! It is a type of online advertising where Ads are exhibited to the users based on their online searches. Most digital businesses use targeted advertising to save money and gain potential customers.
The algorithm of target advertising is based on keyword matching. The NLP model catches such keywords and phrases to associate with the Ads. Other factors, such as recent website visits and frequently opened web pages, impact the target advertising algorithms. However, the entire circle of keyword watches is driven by NLP.
Hiring and Recruitment
Using Natural Language Processing, the recruiters can find the suitable candidates easily and conveniently. The techniques such as name entity recognition and information extraction run by NLP are used to extract location, name, skills, and experience. Further, these features can be used to identify the fit and not-fit candidates.
This unbiased resume filter and selection process reduced approximately 80% of the manual labor. Many enterprises also utilize the NaturaL Language Processing software like ATS (Applicant Tracking System) to screen resumes efficiently.
Social Media Monitoring
Every business’s potential customer might be available on social media platforms to maintain a digital presence. Their daily feeds and posts generate massive data that shows the user’s buying patterns, customer behavior, likes, and dislikes. Here, the NLP techniques can help businesses analyze social media posts and extract relevant insights on them. Social media monitoring also allows enterprises to examine the product issues faced by the users.
Customer service and experience is the most crucial part of any business. We have already discussed the uses of Natural Language Processing in smart assistance and chatbots; however, along with upgrading customer experience, NLP also saves your business cost of hiring customer representatives.
From recommending a product to collecting product feedback, chatbots act as a perfect companion to modern customers.
For instance, Mudra is a chatbot app that provides budget management solutions to millennials, thus reducing costs and revolutionizing the traditional financial money management process.
Automatic text summarization is pretty self-explanatory. The feature helps summarize text by extracting the most important functions and keywords. The end goal is to simplify the process of going through vast amounts of data, including legal documentation, scientific papers, news content/articles, etc.
There are two standard NLP techniques enterprises use to summarize data :
- Extraction-based summarization – This extract key phrases and creates a summary without enhancing the text and adding extra content
- Abstraction-based summarization – This creates new phrases by paraphrasing the original content. This approach is more common and performs better in automating business processes.
The uses of Natural Language Processing are as extensive as you use them. NLP also helps businesses detect urgency in the text. The NLP-based urgency detection model is customized and trained by enterprises to recognize certain words and expressions denoting discontent and gravity.
This enables enterprises to prioritize the most critical customer requests so that they don’t get buried under the pile of unresolved tickets. Urgency detection also improves the business response time, leading to maximum customer satisfaction.
The powerful benefits of AI and NLP are not just limited to detecting urgency on text. In the current digital landscape, NLP based applications and software are being leveraged in every industry for every aspect of emergency management.
One prominent example of urgency detection in the healthcare industry is AI- and NLP-based YouCOMM app that provides real-time access to medical help. It helps connect hospital patients with in-house nurses in case of urgency or emergency medical treatment.
The combination of Natural Language Processing and Artificial Intelligence is what every enterprise needs to run the business more efficiently. There are infinite NLP software and tools available that can be customized to every business’s needs and processes. However, it would be best to seek professional expertise from a reliable AI and ML software development company to gain tailored analytics and insights.
How can Appinventiv help you with Top NLP solutions?
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