Why NLP is a must for AI Chatbot?

Overview

In recent years, we all have heard about chatbots and how useful they can be for employees, business owners, and customers. Despite what we've come to expect and the fact that their behaviors are mostly limited to scripted chats and responses, the future of chatbots is life-changing.

Although regular usage may not necessitate more than rapid answers and simple responses, it is essential to comprehend how far chatbots have progressed and how Natural Language Processing (NLP) can increase their capabilities.

This function offers multiple rewards, truly putting the "chat" in a chatbot. In this article, we will discuss the fundamentals of chatbots also how Artificial Intelligence (AI) is increasing the capabilities of a chatbot.

What is NLP?

Natural Language Processing (NLP) is a branch of computer science that includes human language and artificial intelligence. It helps machines to process and understand human language in order to do repetitive activities automatically.

It facilitates developers in organizing knowledge for tasks like machine translation, Named Entity Recognition (NER), Spell check, automatic summarization, ticket classification, relationship extraction, and topic segmentation.

NLP integrates computational linguistics (human language rule-based modeling) with statistical, machine learning, and deep learning models. These technologies, when combined, allow computers to analyze human language in the form of text or speech data and 'understand' its full meaning, complete with the speaker's or writer's intent and sentiment.

NLP enables computer programs that translate text from one language to another, respond to spoken commands, and summarise vast amounts of text material quickly—even in real time. You've probably encountered NLP in the form of voice-controlled GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences.

However, NLP is increasingly being used in corporate solutions to assist expedite business operations, boost staff productivity, and simplify mission-critical business processes.

To gain a better understanding of NLP, consider sentiment analysis which employs natural language processing to detect emotions in text. This classification task is one of the most common NLP tasks, with firms frequently using it to automatically detect brand sentiment on social media.

Analyzing these interactions can assist brands in detecting significant consumer issues that require immediate attention or in monitoring overall customer happiness. Apart from that, knowing python basics is also a key factor in mastering NLP.

Use cases of NLP

*Sentiment Analysis
*

NLP is used in sentiment analysis to classify the nuance of statements as positive, negative, or neutral, and to monitor the public's general sentiment in social media conversations about your business or product. Sonar employs natural language processing (NLP) to correctly analyze which exact terms related to your brand or product have nuance, allowing you to make data-driven decisions to avoid future crises.

Information Extraction (IE)

Information extraction (IE) is one approach of dealing with unstructured text data using NLP. IE assists in retrieving and organizing preset information in a database, such as a person's name, event date, phone number, and so on.

• Machine Translation: NLP is used by translation programs such as Google Translate to offer contextual meaning and capture the tone and intent of the original text, rather than just replacing words in one language with words in another.

• Intelligent Document Processing (IDP): Intelligent Document Processing is a technique that automatically collects data from various documents and converts it to the appropriate format. It uses natural language processing and computer vision to recognize useful information in a document, classify it, and extract it into a standard output format.

• Question answering: Virtual assistants like Alexa, Siri, and Cortana as well as ML-based chatbots, extract answers from unstructured sources for natural language inquiries. Such dialogue systems are the most difficult to implement and are regarded as an unsolved topic in NLP. This suggests that there's a lot of study going on in this field.

Chat-bots

With advanced NLP techniques, Chatbots have evolved more human-like. With an understanding of how conversations often occur within a context, you can teach your bots to perform domain-specific dialogues without having to collect substantial data and manually categorize them before beginning to train for your specific business.

Chatbots are equipped with Natural Language Generation skills, allowing them to interact with a human consumer or client and solve or grasp their problem before a human executive can take over. Chatbots are programmed with potential inquiries and replies. Companies such as Zomato have efficient chatbots that can answer a wide range of questions.

Language Models

Language models are AI models that focus on NLP and deep learning to determine how to generate human-like text and dialogue. Machine interpretation, grammatical form (PoS) labeling, handwriting recognition, optical character recognition (OCR), penmanship acknowledgment, and other applications use language models.

GPT (Generative Pre-trained Transformer) developed by OpenAI and LaMDA by Google are two well-known language models. These models were created using enormous datasets from the web and other sources in order to automate tasks requiring language comprehension and technical expertise. For example, GPT-3 is the most talked-about AI, with the largest neural network ever developed and the ability to mimic human writing.

Automated speech/voice recognition

Voice recognition is a type of software that translates human speech from its analog form (acoustic sound waves) to a digital form that machines can identify. It is also known as automatic speech recognition (ASR) and speech-to-text (STT). Today, cell phones incorporate speech recognition into their systems to conduct voice searches (e.g., Siri) or to improve messaging accessibility.

Machines interpret a spoken text by first constructing a phonetic map and then determining which word combinations fit the model. It examines the entire context using language modeling to determine which word should be placed next. This is the basic technology behind subtitle-creation tools and virtual assistants.

Email Classification

It is nothing new that our inbound emails are divided into three categories: primary, promotions, and spam. Have you ever wondered? How does it occur? Here, NLP comes into the role. It is essentially a classification problem. The AI is trained to recognize the type of inbound mail based on previous data and other characteristics.

Spam mail frequently contains useless messages and confusing outbound links. Similarly, commercial and promotional emails frequently include promotional content such as discounts and discount offers. Such messages can be discovered using NLP. The classification can be made by AI based on the text content.

Computer Assisted Coding (CAC)

Computer Assisted coding (CAC) tools are a type of software that filters medical documents and generates medical codes for certain terms and terminology found within the document. NLP-based CACs screen unstructured healthcare data to identify features (e.g., medical facts) that justify the assigned codes.

CAC collects data on procedures and therapies in order to grasp every potential code and maximize claims. It is one of the most common applications of NLP, yet its adoption rate is only 30%. It improved coding speed but fell short of precision.

Search Engines

When you search for any particular term or topic in search engines like Internet Explorer, Google Chrome, Mozilla Firefox, Opera, or Safari, NLP machine learning is what enables these engines to understand the intent behind each word and propose the most relevant topics or themes in context.

The results will progressively alter over time based on what's now popular, which is why you could be surprised by the on-point accuracy of the suggested topics connected to your initial query.

What are AI Chatbots?

Artificial intelligence chatbots are chatbots that have been programmed to converse in human-like ways through the use of natural language processing (NLP). The AI chatbot can read human language as it is written using NLP, allowing them to operate on its own.

In other words, AI chatbot software can understand a language other than pre-programmed commands and respond depending on previous data. This allows site users to take the lead and express themselves in their own words.

Moreover, AI chatbots are always learning from their conversations, allowing them to adapt their responses to various patterns and new scenarios over time. This means they can be used for a variety of purposes, such as analyzing a customer's emotions or predicting what a site visitor is looking for on your website.

Chatbots are employed in a wide range of industries for a variety of reasons. There are retail bots that pick and order groceries, weather bots that provide daily or weekly weather forecasts, and friendly bots that just converse with folks in need of a friend.

Take an example of Meta (as Facebook's parent company is now named), which features a machine learning chatbot that creates a platform for businesses to connect with their clients via the Messenger app. Users may use the Messenger bot to buy shoes from Spring, get a ride from Uber, and converse with The New York Times about current events.

If a user asked The New York Times through the app a question like, "What's new today?" or "What do the polls say?" the bot would respond to the request.

In 2016, Thinking Capital, a small company lender in Montreal, used a virtual assistant to provide customers with 24/7 support via Facebook Messenger. A small business seeking a loan from the company just needs to answer essential qualification questions posed by the bot in order to be considered eligible for up to $300,000 in financing.

Why NLP is a must for AI Chatbot?

Chatbots can conduct human-like conversations if they have context awareness. And training chatbots with NLP is important for this skill. Chatbots can readily interpret complicated human language using NLP. Everyone has a unique way of expressing themselves. Chatbots can easily understand a person's personality and reply accordingly using NLP. Also, NLP allows chatbots to better grasp sarcasm, humor, and other conversational tones. To be honest, NLP gives chatbots a personality of their own.

NLP will help chatbots in interpreting the raw text, processing it, and deliver enriched information to users using computational linguistics, context extraction, content summarization, and sentiment analysis.

Chatbots are designed for a variety of reasons, like FAQs, customer support, virtual aid, and much more. Chatbots that lack NLP relies heavily on pre-fed static data and are therefore less capable of dealing with human languages that vary in emotions, intent, and feelings to communicate each individual inquiry.

Adding NLP skills to your Chatbot is simple and inexpensive. There are numerous chatbot technologies available, such as Google TensorFlow, Rasa, Google Dialogflow, and Dr. Watson, that allow us to create AI/NLP-based Chatbots.

Let's look at six reasons why NLP is a must for AI Chatbot:

1) Market Research and Analysis:
Social media alone can provide or generate a significant amount of versatile and unstructured content. The information obtained from the Chatbot NLP contributes in the structuring and interpretation of unstructured content. You can quickly grasp the meaning or concept underlying the customer reviews, inputs, remarks, or queries.

One can also obtain a sense of how the user feels about your services or brand, which can assist in important decision-making and improvements in processes within the organization.

2) Improvised Customer Experience:
The Chatbot can be programmed to respond to a variety of consumer inquiries. Today's customers want rapid responses and answers to their problems. NLP enables chatbots to understand, analyze, and prioritize questions based on their complexity, allowing bots to answer client queries faster than human beings.

As a result, the response time could be minimized, resulting in more effective communication with the customer and an overall better experience. Faster replies contribute to the development of customer trust and, subsequently, greater business.

When you use NLP chatbots, you will see an increase in customer retention. It decreases the time and cost of acquiring a new customer by increasing the loyalty of existing ones. Chatbots give customers the time and attention they need to feel valued.

3) Natural Conversations over languages:
NLP does not only allow machines to grasp the human language and speech structures. It can also recognize idioms and slang and determine the correct meaning for each word. As a result, NLP technology provides natural, effortless conversations between humans and bots. Furthermore, chatbot technology has the ability to learn from previous encounters, which means that it becomes smarter and more accurate every day.

NLP can distinguish between different types of human-generated requests, significantly improving customer experience.

• Natural language processing (NLP) chatbots are capable of comprehending language semantics, text structures, and spoken phrases. As a result, it enables you to make sense of large amounts of unstructured data.
• As NLP is capable of understanding morphemes across languages, a bot is more capable of understanding diverse nuances.
• NLP enables chatbots to understand and interpret slang, learn abbreviations, and recognize diverse emotions through sentiment analysis, just like humans.

4) Reduced Costs:

Costing is a key component of every business's growth and increase profitability. One of the most significant advantages of NLP in business is its capacity to automate manual and repetitive processes. NLP-based chatbots can greatly reduce the costs associated with manpower and other resources involved in repetitive tasks. In terms of budgeting, its integration results in lower expenses gained through streamlined operations and overall business efficiency.

5) Focus on Mission Critical Tasks:

For a company to function, many different roles and resources are deployed, which necessitates the repetition of manual tasks across many verticals such as customer service, human resources, catalog management, or invoice processing. NLP-based chatbots drastically reduce human labor in processes like customer support or invoice processing, using less resources while increasing employee efficiency.

Employees can now focus on mission-critical tasks and tasks that positively benefit the business in a significantly more creative manner, rather than wasting time on monotonous repetitive tasks every day. NLP-based chatbots can also be used internally, particularly in Human Resources and IT Helpdesk.

6) Smart Resource Allocation

NLP is generally used for performing repetitive tasks. It turns out to be a major benefit for business owners when 'trained' technology executes minor jobs while human operators focus on mission-critical ones. In other words, having a chatbot that handles repetitive tasks allows you to spend valuable human resources more effectively.

Conclusion

• Natural Language Processing (NLP) is a branch of computer science that includes human language and artificial intelligence. It helps machines to process and understand human language in order to do repetitive activities automatically. NLP overcomes the communication gap between complex human language and coded machinery language.

• Natural language processing is one of the most important fields of Artificial Intelligence, and it's already used in many of the services we use every day, from chatbots to search engines.

• The future of chatbots is really incredibly bizarre. It's amazing how intelligent chatbots can be if you take the time to train them and provide them with the data they require to evolve and make a difference in your business.

• Businesses are using NLP to automate some of their daily activities and make the most of their unstructured data, gaining actionable insights to enhance customer satisfaction and create better customer experiences.

• NLP can be used to determine what the user is actually trying to say or question. This allows brands to engage with their customers in a more personal, empathic manner, which can eventually make them stand apart from their competitors.