Businesses today deal with an avalanche of texts (contracts, documents, customer service, social media) which means potentially a lot of data to benefit from. How effective are you and your team in handling many texts? NLP allows you to spend less time and money you would otherwise spend doing strenuous work. Enjoy the freedom and receive happier customers and employees.
You bet your industry leaders are already on it, so let’s dive deeper into it to figure out natural language processing use cases and benefits.
What is natural language processing (NLP)?
First, let’s define what natural language is. It is just a fancy way of describing how humans pass information between each other through talking/writing.
Natural language processing (NLP) technology allows machines to understand human language. Its algorithms recognize texts and can edit, summarize and classify them. NLP is characterized as a difficult problem in computer science. Human language is generally neither precise nor plainly spoken. Moreover, in terms of data science, it is unstructured text data. Understanding human language means not just recognizing the words, but also perceiving the ideas, and concepts, and how they’re linked together to create meaning.
How to benefit from natural language processing (NLP)?
Finding areas of your business for natural language processing application is a piece of cake compared to other AI implementation fields. You just have to find processes in your business where text is being processed by people or people and machines in collaboration. The complexity of the processes may significantly vary, for example, copying a phone number from an email signature vs. deciding if any legal conflicts between two contracts occur. Thus, whether the process under consideration is worth automation using artificial intelligence is the main question. To find it out, you have to discuss four main questions from the picture below, and after getting positive answers for the first one, you can move to the next one and further. In the end, you have to get a positive response for each of them (read more in the AI life cycle guide).
To sum up, like all AI-based solutions, NLP-based projects are often built to handle the most time-consuming, mundane, or routine tasks, such as:
Answering repetitive questions
Acting as customer support
Monitoring for references to a company or its services/products on social media
Scanning documents for keywords and filtering them (for example, CVs)
Classifying emails (e.g. , as spam)
General NLP applications
As mentioned before, natural language processing will be helpful for businesses that deal with large amounts of unstructured text, such as emails, social media conversations, online chats, survey responses, and many other forms of textual data. So let’s look closely at how NLP could manipulate human language.
1. Text classification
This core task of natural language processing aims to go through the text and label it based on its content and detected keywords.
3. Machine translation
AI translates the text from one language to another without human involvement. Deep learning networks translate the words in the source text based on the localization and context of that word used in the sentence.
5. Named entity recognition
This application identifies named entities from a text and assigns them into pre-defined categories. For example, named entities can be names of people, universities they attended, companies they worked at, dates, and quantities.
Simply put, chatbots are AI programs that can simulate human conversation or chat through messaging interfaces. Thus, first, they use NLP to understand human language.
2. Sentiment analysis
As a subfield of text classification, it processes the text to classify the author’s sentiment towards something.
4. Text mining
Text mining aims to discover relevant information (unknown and possibly hidden in the context) in a text by transforming the text into data. Text mining tools provide detailed information about the text itself (meanings, etc.) and highlight patterns across the vast data set.
6. Predictive text modeling
The algorithm learns to predict the next word in a sentence by understanding the localized context of that sentence.
8. Recommender systems
Content-based recommender systems provide suggestions for items that could be the most relevant for the exact user and rely on two approaches: item-item similarity and user-user similarity.
Natural language processing: industry use cases
NLP applications are theoretically possible in any field or industry, dealing with textual data of every kind (contracts, social media, e-mails, lists of goods, et cetera, you name it). Examples below could be transferred or added to other industries. However, each use case should be routine, deal with vast data, and include repetitive tasks.
End-user customer support automation is a flourishing field for NLP implementation: conversational AI as chatbots or callbots. Human speech recognition via NLP enables them either answer the users’ questions or direct them to the most qualified person. In addition, with the help of natural language generation, you can create virtual assistants. Any of these use cases can reduce customer service costs and increase interactions handled.
For example, the leading Northern European telecommunications company Elisa implemented the chatbot Annika, which handles 45% of all the inbound contacts and fully resolves customer contacts with 42% FCR.
2. NLP in finance and banking
Banking and financial institutions can use a broad spectrum of NLP applications to reduce risks and make better decisions.
Sentiment analysis for a better financial decision-making
The stock market is sensitive to news and world events. So, for example, NLP may go through financial articles, tweets, social media posts, and stock market opinions on StockTwits, extracting the relevant information. That will provide their financial analysts with meaningful insights on the market moods and trusted and questioned investments or authorities.
Voice money transfer
Virtual assistants in banks are old news, but their capabilities improve over time. For instance, the Royal Bank of Canada offers its clients voice money transfers. The technology, based on NLP, is activated by voice, and first, it recognizes a client’s voice and speech, then it generates human voice feedback. However, the clients call the name of the contact and the sum but also have to approve the operation via bank application and finalize it with Touch ID. Thus, the security level is considerably high. Furthermore, no words are needed to explain that level of clients’ satisfaction and loyalty rises up with this NLP application: the less annoying interactions with passwords and manual typing, the better.
Intelligent document search
Performing grounded financial decision-making often requires extra information that should be relevant. Thus, NLP algorithms can find everything relevant in free text data. However, those solutions are based on detecting patterns in large volumes of unstructured data.
3. NLP in insurance
Insurance companies may apply NLP to identify and reject fraudulent claims, as banks do with credit claims. Analysis of customer communication indicates fraud and flags suspicious claims for deeper investigation. However, the same natural language processing technology may also be used for competitor research.
NLP automation to fill required data
Speaking of annoying repetition of password (or other information) input: this situation could be improved with NLP. Just like other websites, insurance websites could ask to collect cookies. It speeds up the process of filling forms to several minutes, saving time (and nerves). Moreover, it also eases the work burden of insurance specialists: the saved data usually contain fewer errors when it is up-to-date.
4. NLP in manufacturing
Manufacturers may rely on NLP to analyze shipment-related information to streamline processes. For example, voice assistants, callbots, or chatbots can receive updates about the delivery time or goods description. As a result, they can quickly spot where improvements are needed and make changes efficiently.
NLP is an alternative for cost optimization: the algorithms can scrape the open web sources for the best pricing of different raw materials and services.
5. NLP in retail
NLP can analyze customer sentiment and provide valuable insights about products and services. Thus, retail companies can assess their processes wisely, making better and more informed decisions. Furthermore, any segment of a retail system could be improved with AI, whether it is product design, inventory management, or marketing. Hence, all available customer data processed with NLP transforms it into actionable insights that can improve the customer experience.
6. NLP in medicine and healthcare
NLP has become a reality in medicine and healthcare.According to the Becker’s Hospital Review, there are three main use cases of NLP in this field :
Mainstay cases are speech recognition, clinical documentation improvement, data mining research, computer-assisted coding, and automated registry reporting.
Emerging cases are clinical trial matching, clinical decision support, risk adjustment, and hierarchical condition categories.
Next-gen cases are an ambient virtual scribe, computational phenotyping, biomarker discovery, and population surveillance.
The StructBERT NLP model integrated with Alibaba’s ecosystem not only understands the context of words in search queries but also leverages the structural information: sentence-level ordering and word-level ordering. By analyzing the text of medical records and epidemiological investigation, the Centers for Disease Control (CDCs) used StructBERT to fight against COVID-19 in China cities.
Smart assist tool
Another example is the Estonian helpline 1247, which, during the coronavirus, was providing the necessary healthcare information to people. Moreover, frequent updates of the situation came up from multiple sources, so it was a relief to get the tool, assisting to find the required information in seconds with the help of AI.
7. NLP in human resources
The use of NLP in HR is expanding at every stage of employee involvement. For example, in the recruitment stage AI model is applicable for sifting multiple resumes, or subsequent analysis of sentiments in employees’ surveys.
In this regard, NLP is useful for HR agencies; for example, Fuzu, a Helsinki-based company providing ambitious young East African professionals with job opportunities, career advice, and new skills, implemented machine learning models to streamline its user onboarding experience and boost engagement through personalized recommendations.
The input data for the recommendation engine is obtained by classifying data from uploaded text documents such as resumes and cover letters.
As a result, the click-through rate for job applications increased by 30%.
8. NLP in law and accountancy
Employees of law offices or accounting firms must review a lot of contractual information. No wonder finding the right place in the needed document can take hours. Creating NLP systems for legal and accounting professionals would reduce the time spent looking for specific clauses.
9. NLP in the public sector
Reaping the benefits of NLP in the public sector leaves no doubt about its value. Alarming public money misuse is just one example. AI, created for the independent Riigikontroll (The National Audit Office), is highly organized to collect data from more than 1000 news sources and aggregate it with the aim of further analysis, consequently developing a risk assessment if specific irregularities trigger some model. These risks are also aggregated and shown to the auditor. The major benefit of AI is performing a risk assessment more efficiently with less human effort.
Another example is Bürokratt, a virtual assistant of Estonia, which helps people to communicate with public agencies, and, if requested, provides an overview of the obligations and opportunities the government offers. It makes the communication between people and the state structures more efficient and accessible through voice-based virtual assistants, offering the best user experience for digital government.
10. NLP in education
As education is closely connected with language and words, obviously, use cases for NLP in education are inevitable.
NLP for the improvement of writing and reading skills
It’s not only students who benefit from NLP: spell-checking apps like Grammarly have already won a firm place in the arsenal of copywriters and managers. It provides actionable advice on how to make text clearer and get rid of mistakes. In addition, the software uses NLP technology to analyze text and provides suggestions for improvements.
NLP can also offer more detailed feedback, for example, pointing out the lack of essential facts or sufficient evidence to back up a point, as well as detecting plagiarism.
There are other potentially ubiquitous examples of the collaboration of NLP and education, like enhancing teachers’ vision of what is happening with their students and their abilities by bringing recommendations on improving a core level of writing skills.
Unfortunately, teachers often lack time or resources to quickly identify problems in each and every student’s reading ability and provide real-time feedback on how to improve. And then, the NLP model is here to aid, which is why NLP solutions are becoming increasingly sought-after.
Or why not try using NLP recommender systems in education to match students with the most suitable reading material, both challenging and increasing productivity? Among other things, NLP technologies will grade student reading scores more accurately than traditional formulas do, such as the Flesch-Kincaid Grade Level test.
NLP is of help in creating educational materials and finding pairs of questions and answers. Another thing it does well is defining how close the student’s written responses are to the facts from the learning material.
It may set up the questionnaire and assessment to a student’s specific learning needs, such as reading level and learning tempo.
11. NLP in marketing
As marketing becomes more and more personalized, businesses are turning to language analytics via NLP to receive insights about customer motivations, intentions, buying journeys, et cetera, from vast qualitative data sets.
Sentiment analysis for understanding customers
As mentioned above, it is possible for AI to obtain the intent and sentiment behind the language. For example, Vibe’s Conversational Analytics platform derives the sentiment of conversations with individual customers and steers the conversation towards a conversion. In comparison, Remesh analyzes sentiments of large groups and direct group conversations. Brand monitoring, competitive research, product analysis, and many others could be use cases for sentiment analysis in marketing.
Chatbots for capturing leads
A business website without a pop-up chat box on the home page offering to assist you is now rare. Currently, these chatbots tend to either come across as a bit wooden once the conversation becomes more complex, or they rely on being able to hand off to human customer support personnel when things become interesting.
Benefits of natural language processing
NLP gives insights from texts that would be unrealistic to get by manually reading because there’s more than people have time to read. To sum up, let’s mention the main benefits of NLP leveraging:
Improves the end-user experience.
When applied to customer service automation, NLP-powered assistants and bots offer immediate customer service that enables natural conversations, thus increasing customer satisfaction and loyalty.
No doubt, routine and repetitive tasks of dealing with texts could be improved in terms of working hours and human resources requirements. Businesses can accomplish more with the same resources; hence, the next benefit comes.
Saves time and money.
Increases employee efficiency and satisfaction.
Although some people doubt automatization and fear that robots or AI would take their place, this fear is unfounded. Robots increasingly take over mundane and repetitive tasks so that humans could provide value in the areas where they are good (and interested most). It means humans take care of more creative tasks requiring estimations, which are rather complex for machines.
Natural language processing (NLP) software is a process running in the background of many typical applications, as it is vital for many applications. However, each and every use case should be carefully assessed and reviewed before the NLP-powered project has even been started. Some cases could be automated with easier (and cheaper) technologies, so it is better to get advice on the use case validation from AI experts. As such a team, we dig deep into the business challenges of the clients to figure out the solution for the best results.
MindTitan customers appreciate that we provide more than just regular NLP services. We also offer consultation on data collection and labeling and assist with source data validation, i.e., determining whether the data is helpful for the task at hand