Terms like chatbots, machine learning, natural language processing and artificial intelligence seem unmanageable ‘in-absence-of’ a plan to convert a complex functionality into a workable affair. Users seldom seek complexity. They wish to buy and facilitate quick and advanced search features, pay their bills, avail discounts, channelize loyalty points, and fulfil their needs. A conversational UI makes up for the rising applications of chat bots by businesses.
Modern computing is deliberately changing active portfolio management for the better. Innovations in artificial intelligence (AI) and natural language processing (NLP) are no exception. These technologies are helping buy-side analysts and fund managers to access datasets, research, and impact the investment process. NLP enables computers to read text, hear speech, interpret it, measure sentiments, and determine other parts. Technologies that tout NLP capabilities come with Boolean operators, algebraic toolkits and some artificial intelligence.
What is Natural Language Processing (NLP)?
Data is omnipresent. It is sometimes structured or unstructured; it needs to channel for a relevant and required outcome. Businesses have plenty of text-based surveys and emails to plow through. Business researchers often use social – media posts for analysis. It makes natural language processing a must-have skill-set for data scientists who look to move the needle to research or their career.
NLP is an interdisciplinary field of computer science and ‘linguistics’ concerned with interactions between machine language and human language like English. NLP software powers:
- AutoComplete: In search Engines (E.g. Google)
- Personal Assistants: Siri, Cortana, Google Assistant
- Spell Checking: in your browser, on social media accounts, in email, within desktop apps (like Microsoft Word), in your integrated development environments (IDE like Visual Studio)
- Machine Translation: Google Translate
What Are The Use Cases of NLP?
Fast food chains take the help of NLP as they receive a vast amount of orders and complaints daily. They manually handle tiresome and repetitive tasks. But conversational artificial intelligence automates processes and reduced human intervention.
Brands launch new products and market them on social media platforms. They measure campaigns’ success rates using reach and interactions. But they require sentiment analysis to understand the consumers’ public sentiment – a text classification task where machine learning models quantify affective states.
How Can Applications of NLP?
- Natural Language Understanding (NLU)
NLP makes computers understand human intent. NLU takes human syntax, sentences, structure and makes decisions based on the way a human speaks. It takes two-way interactions as question/answer processes, searches, chatbots, and other platforms where an average person asks a question;
- Sentiment Analysis
Sentiment analysis captures the emotions behind it. It helps B2C businesses for social media discussions regarding the product. It helps figure out if your customers are glad about your service offerings.
- Machine Translation
ML makes machines convert the rich text from one language to another through a translation app. You’ll be intrigued to see how applications translate word-by-word and substitute them at a very standard level without losing the context/sentence structure during the translation.
- Chatbots
Chatbots and virtual assistants are ubiquitous across countless businesses, events, government sites to automate their interactions with customers to save time, money, resources. Custom chatbots are in high demand as this eases the tasks quickly.
- Semantic Parsing
ML and NLP parse the natural language data into machine understandable language.
What Are The Various Types of Chatbots?
An AI chatbot on an e-commerce site completely differs from a chatbot on a banking site. Consider the following types as you build NLP chatbots:
- Menu/button-based – To answer FAQ and support queries.
- Linguistic-based (Rule-based) – To create conversational flows using if/then logic. These define language logic conditions.
- Keyword recognition-based – Such chatbots utilize customizable keywords and an AI application and Natural Language Processing (NLP) to determine how to serve an appropriate response to the user.
- Machine Learning chatbots (Contextual chatbots) – Such chatbots remember conversations with specific users to grow over time. Such chatbots with contextual awareness help self-improve based on what users ask for and how they are asking it.
- The Hybrid Model – It combines the simplicity of rule-based chatbots with the complexity of AI bots.
- Voice bots – Voice-activated chatbots bring frictionless experiences directly to the end customer.
What Are Some Of The Applications of Chatbots?
Appointment Scheduling or Booking Bots
- HR Bot for scheduling meetings and interviews
- Healthcare Bot for booking appointments
- Travel Bot for flight bookings
- Number of active users
- Hotel Booking Bot to book rooms and services
- Number Appointment and Slot Booking is integral with any business
- Cinema Bot to book movie tickets
- Service Bots for automotive businesses
- Number of active users
Customer Support Chatbots
- Retail Support Bot;
- Telecom Bot;
- Techdesk Bot;
- Banking Bot;
- Orders, deliveries, and logistics Bot;
Marketing and Sales Chatbots
- eCommerce bot;
- Education-course bot;
- Automotive Lead Generation bot;
- Real Estate bot;
- Quiz bot for market research bot;
- Social media marketing bot;
- Lead generation bot;
- Lead generation with Salesforce;
Entertainment Bots
- The TV show guide;
- The Go-karting bot;
- Quiz bot;
- Riddle bot;
- News and media bot;
- Cinema bot;
- The entertainment factor;
- Youtube channel bot;
- The podcast bot;
Conclusive: Why Does Your Chatbot Need Natural Language Processing?
Chatbots are a great way to engage online visitors by interacting with them in their natural language. NLP-based chatbots communicate with customers through textual or sound methods. Such programs support clients on websites or via phone. The chatbots are generally messaging applications, either inbuilt within an e-commerce or a banking platform or are independent like Facebook Messenger, What’s App Messenger, Slack or Telegram.
Natural language processing helps chatbots understand, analyze, prioritize the complexity of questions. It also enables bots to respond to customer queries faster than human beings. Such faster responses from such virtual assistants help build trust and generates more business. Avail our experts for all relevant NLP chatbots queries!