I have been super interested in Machine Learning and how it will work with us in the future. NLP recently came up so I thought I would do some research and share some knowledge. My plan is to expand on each of these if this proves to be a popular topic.
So here we go to start. This a simple article on the most frequently encountered examples of Natural Language Processing.
1. Sorting emails with filters
NLP has been utilized for many years for its email filtering abilities. Initially, there was some inaccuracy, but with the utilization of machine learning on massive amounts of data, emails are now sorted correctly much more often.
2. NLP in Virtual helpers, vocal aides, or intelligent speakers
Apple’s Siri and Amazon’s Alexa are two of the most widely-known virtual assistants. These applications deploy natural language processing (NLP) and machine learning to interpret and manage voice commands. Furthermore, custom training is possible without extra effort due to NLP algorithms, enabling the assistants to learn from past interactions, relate queries, and link to other applications.
It is predicted that the utilization of voice assistants will increase dramatically, as they are employed to manage home security systems, thermostats, lighting systems, and automobiles, and even alert you when something is running low in the refrigerator.
3. Utilizing the web for research
Web search engines have become a great asset for quickly obtaining information. They provide a simple and efficient way to locate data, allowing users to access various sources with just a few keystrokes. With the help of these engines, it is now much easier to find the answers to questions and to research any topic with minimal effort.
A Google search involves NLP machine learning. The algorithms employed are highly trained and can detect the purpose of the query. The results can be different daily, adapting to new trends and how people speak. Additionally, the search engine can suggest related topics to ones you might not have known you were curious about.
4. Text that can anticipate what the writer intends to say
Natural Language Processing (NLP) is in effect whenever you use your cell phone to type a message. By entering a few letters of a word, the texting application will suggest the correct completion for you. As you use the app more often, it becomes more precise and can recognize words and names that you frequently use before you can even type them.
Word processing programs such as MS Word and Google Docs have advanced to a level where predictive text, autocorrect and autocomplete are so precise that it can leave one feeling like they need to relearn grammar basics.
5. Gauge opinion on social media about specific brands
The process of sentiment analysis is used to classify opinions expressed in a text as positive, negative, or neutral. It is often used for tracking online comments about a brand, product, or feature, or for comparing a brand to others in the same industry. This technique of sentiment analysis is especially popular when it comes to monitoring social media.
Creating a new product and wanting to know what your customers think? If there is a disgruntled customer who tweets their dissatisfaction with your customer service, sentiment analysis can help identify these negative comments immediately so you can take swift action to address them.
6. Rapidly classifying client feedback
Organizing qualitative feedback such as product reviews, surveys, and social media conversations into relevant categories can be achieved using text classification. This Natural Language Processing (NLP) task entails the assignment of predefined tags to a text based on its content.
Recently, a Software as a Service platform utilized Natural Language Processing instruments to sort out Net Promoter Score responses quickly, granting actionable knowledge: as detailed in this blog post from Monkeylearn.
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7. Streamlining customer service tasks through automation
Integrating AI-powered technology has significantly reduced the necessity for manual tasks in customer service, which has streamlined processes and saved agents valuable time. This concept, referred to as customer service automation showcases the practical uses of Natural Language Processing.
The Zendesk benchmark reports that tech businesses get over 2,600 inquiries for help each month. Having to manage all the different requests that arrive from multiple sources (email, social media, live chat, etc) implies that companies need to have a system in place to sort out each incoming ticket.
Organizations have the capability to utilize text classification in order to label customer support tickets automatically by their subject, language, sentiment, and/or priority. Subsequently, we can assign the tickets to the most appropriate group of agents depending on these tags.
Uber created a ticket routing system that labels tickets by Country, Language, and Type (with sub-tags such as Driver-Partner, Questions about Payments, Lost Items, etc), then applies some prioritization rules. For example, requests from new customers (New Driver-Partners) get the highest priority.
8. Utilizing Automated Conversational Agents
We design chatbots as automated agents for engaging in conversations, and they fulfill a wide range of objectives. They can respond to user inputs in real time and prove helpful for customer service, offering automated assistance, and even for amusement.
We refer to computer programs that can simulate human-like conversations as chatbots. They utilize Natural Language Processing (NLP) to understand the purpose of a sentence and recognize relevant topics, keywords, and even emotions in order to generate an appropriate response based on the data they have interpreted.
Customers expect fast, tailored, and all-day service from businesses, and chatbots have become a central part of customer service strategies to meet this demand. By offering quick answers to questions, chatbots can reduce the wait times of customers and handle a high volume of routine inquiries. Astonishingly, chatbots are capable of resolving up to 80% of routine customer support tickets according to IBM.
Chatbots are able to do multiple tasks, including customer support, recommending products, offering discounts, and making reservations. To achieve this they employ an ‘if/then’ logic, which involves programming them to identify intents and associate them with predetermined actions. Alternatively, they can also offer a selection of options for the user to choose from.
9. Generating summaries automatically
We refer to the process of creating a concise summary of extensive unstructured material, such as research, as automatic summarization. This technique is beneficial for providing an overview of the content.
NLP can be used to summarize content in two distinct approaches.
To obtain the important facts from a text and form a summary(extraction-based summarization), one can use deep learning methods to paraphrase the text and create sentences that are distinct from the original source(abstraction-based summarization).
When it comes to data entry, automated summarization can be extremely helpful in taking the important details from a product description and entering them into a database.
10. Translating by Machine
Machine translation changes text from one language to another using software to understand the source. It then recreates content in the target language. It has become an important tool for businesses and individuals to bridge language barriers quickly and accurately.
Since the 1950s, converting text and speech into different languages has been a topic of great interest in NLP. This started with attempts to convert Russian words to English. Despite considerable advancements in MT, there remain challenges that require resolution.
A few of the top-ranking machine translation platforms are Google Translate, Microsoft Translator, and the Facebook Translation App. The English-to-German translation model of Facebook AI was granted first place in the 2019 WMT (Conference of Machine Learning) competition. The organizers deemed the translations produced by this model as “superhuman” and significantly superior to those crafted by human experts.
Machine translation has seen the development of customizable systems, tailored to particular areas such as healthcare, law, and finance. An example of this is Lingua Custodia, a machine translation tool dedicated to translating financial documents.
11. Generating sentence structures using natural language
The branch of Natural Language Processing (NLP) known as Natural Language Generation (NLG) is devoted to the construction of computer programs that can automatically generate any type of text in a natural language from a semantic representation as input. Two of the areas where NLG is being used are question-answering text summarization.
In 2019, Open AI, a company that focuses on artificial intelligence, revealed GPT-2, a revolutionary text-generating system. An advancement in AI made possible by a gigantic dataset of 8 million web pages. With only minimal guidance, GPT-2 is capable of producing insightful and high-quality writings, such as articles, stories, or poems.
The model yields better results when fed with topics that have a large amount of data. However, Brexit does not have the same success with niche or highly technical content. However, we have not fully seen its potential yet.
Final Words on Natural Language Processing
NLP is used in many aspects of life, including building a business around a product, improving customer service and engaging customers, developing websites, documenting, editing, producing videos, improving writing skills, etc. This article focuses on NLP applications in customer service and how to use NLP to improve customer service.
Conceptually, NLP is the science of effectively understanding and using natural human language. This is achieved through the analysis and modeling of natural human language. Natural language has always been a powerful and effective tool for conveying complex, nuanced, and subjective messages to others. NLP tools, when used, give your business and service a competitive advantage in the marketplace.
Perhaps what is most impressive about NLP is that it has been around for over 35 years. Yet, many people have not even heard of it. In fact, the NLP field is often called the “Oreo of fields,” as it’s been working very hard in the shadows for much of its history.
The benefits of using NLP to improve customer service are numerous, and this article provides a few examples of how to do it.
Look for more articles on the above 11 bullet points in the future!
