Chatbots have been a part of the IT world for quite some time, yet they continue to be a topic of interest. Surveys indicate that 34% of consumers believe chatbots assist in connecting them with human customer service, this further highlights the usefulness of these tools. The introduction of natural language processing (NLP) has significantly improved daily life, evidenced by the fact that 84% of consumers use NLP at home, and 27% utilize it in their workplaces.

Thanks to advancements in NLP technology, chatbots have evolved to engage in conversations that are strikingly similar to human interactions.

What is a chatbot?

Chatbots are software solutions designed to simulate human conversations. They excel at completing tasks, achieving goals, and delivering results effectively. Thanks to natural language processing and machine learning, the conversations facilitated by chatbots are virtually indistinguishable from those with humans.

By utilizing advanced algorithms, chatbots are equipped to comprehend natural language and provide contextually relevant responses. In various fields, ranging from customer service to healthcare, chatbots are revolutionizing the way individuals engage with technology, simplifying many aspects of daily life.

What is an NLP chatbot?

NLP or natural language processing plays a pivotal role in facilitating human-to-machine communications, eliminating the need for humans to master programming languages like Java. The distinction between the language used by humans for interpersonal communication and the programming language or the chatbot leveraging NLP is significant.

NLP serves as the bridge, translating human language, such as English, into a format that machines can comprehend, without requiring the intricate knowledge of programming languages like Java.

How to build a chatbot using NLP?

This article outlines steps to consider when building a chatbot using Natural Language Processing (NLP):

1. Analyze the business logic

The analysis of business logic is essential for developers to understand their clients thoroughly.

To achieve this understanding, a discovery phase is undertaken. This phase involves analyzing the business logic, examining the competitive market, and outlining the essential features that the future product should possess. Following these steps leads to the creation of a solid business logic for the product, ensuring a comprehensive assessment of its relevance and potential in the market.

2. Stack of technologies and channels

Several platforms and frameworks are available for chatbot construction, including popular options such as BotPenguin, Dialogflow, Botpress, and Rasa.

For those interested in developing a voice chatbot, the BotPenguin platform is often recommended. In contrast, Telegram, Viber, or Hangouts serve as suitable channels for crafting text chatbots.

When it comes to the technical stack essential for chatbot development, the most common technologies include:

• Python – A programming language employed to design the architecture of the chatbot.
• Pandas – This involves libraries for data manipulation and analysis in Python.
• Twilio – A service that allows software developers to make and receive phone calls, send and receive text messages, and perform other conversational activities through its APIs.
• TensorFlow - Utilized for machine learning and neural network tasks, this library is a favored choice among developers.
• SpaCy – Known for its advanced natural language processing capabilities, it is an open-source library.
• Telegram, Viber, or Hangouts APIs – These APIs are crucial for integrating the chatbot with various messengers or websites.

3. Integration of NLP and development

The initial steps in creating a machine learning chatbot involve the development of a client-side bot and its connection to a provider’s API, such as Telegram, Viber, Twilio, among others. Incorporating NLP (Natural Language Processing) through the application of artificial intelligence can be considered once the development phase is completed.

4. Testing

Testing the chatbot with actual users and making adjustments based on their feedback is crucial. Manual testing is a viable option due to the limited number of scenarios that require checking. This process is crucial in determining the performance of the AI NLP chatbot.

How does NLP work in a chatbot?

An NLP chatbot offers significant assistance by allowing customers to simply type their requests, reducing confusion and frustration. Incorporating various techniques is essential to add more dimensions to information processing in language management. Understanding various principles of language processing is crucial at the beginning of working with NLP.

The process involves four key steps:

• Morphology: This determines the structure and relationship of words.
• Syntax: The Syntax dictates the order of words to form coherent sentences.
• Semantic studies: It reveals the meaning of words through lexical and grammatical structures.
• Pragmatics: It deals with the contextual meaning of words.

Primarily, adapting the language for computational understanding involves using syntactic and semantic analyses to make data interpretation and cleaning more efficient.

5 Best AI chatbots with NLP

Top AI Chatbots Utilizing NLP Technology

1. BotPenguin

BotPenguin is an AI-powered chatbot platform that leverages natural language processing (NLP) to deliver automated chat management. This platform is adept at handling a wide array of typical customer inquiries, such as questions about return policies, purchase status, cancellations, and shipping fees.

The platform boasts integration capabilities with a variety of systems and services, including Groove, Jira, Help Scout, Time Trade, HelpDesk, Zendesk, Salesforce, Zapier, Zoho Mail, Google Sheets, Facebook, Skype, and many others.

BotPenguin also offers a range of features without any initial charges, allowing users to explore various options for free.

BotPenguin Features:

• Natural Language Processing
• Zero Development Time
• Zero Setup Cost
• Conversational UI
• Deep Analytics
• Easy to Integrate
• Cost-Effective
• Easily Customizable
• Strong Validation
• Human Handover
• Multiple Messaging Channels
• Branching
• Capability to Learn

BotPenguin facilitates the creation of chatbots for various platforms.

2. Haptik

Haptik stands as a leader in the Indian market, offering an enterprise conversational AI platform tailored for businesses. This innovative platform, powered by natural language processing (NLP) technology, enables businesses to digitize and replicate their customer engagement experience across a variety of messaging platforms.

The tool is adept at crafting, developing, and maintaining sophisticated chatbots, empowering businesses to streamline their customer service operations. With its comprehensive dashboard, users gain access to detailed chat analytics, and insights into conversations as well as enhance customer interaction strategies. Moreover, Haptik seamlessly integrates with existing or custom CRM systems, offering a flexible solution to enterprises.

Key Features of Haptik Include:

• Natural Language Search
• Performance Metrics for evaluating effectiveness
• Personalization options for tailored interactions
• Pre-Configured Bots for ease of use
• Real-Time Analytics, Data, Monitoring, and Notifications
• Advanced Reporting & Statistics
• Robust Search/Filter capabilities
• Sentiment Analysis for understanding customer emotions
• Social Media Integration for broader reach
• Speech Recognition and Synthesis for versatile communication channels
• Tools for Surveys & Feedback collection
• Third-Party Integrations for expanded functionality
• Efficient Transfers/Routing to ensure customer inquiries are directed appropriately

While it doesn’t adopt a fixed pricing model, Haptik provides a scalable solution designed to meet diverse business needs, promoting efficient and effective customer engagement.

3. Kore.AI

Kore.ai stands as a pioneer in the conversational AI space, offering an all-encompassing, AI-enhanced “no-code” platform designed to streamline communication. The Kore.ai NLP chatbot is engineered to facilitate quicker, more intuitive, and human-like interactions.

This platform excels in creating, training, testing, and evaluating AI and NLP-powered chatbots, ensuring they’re optimized for a broad spectrum of consumer and business communication channels.

Kore.AI extends its compatibility to IP phones, wearables, and various speech recognition devices such as Amazon’s Echo, seamlessly integrating with platforms including Skype, Cisco Spark, Slack, Workplace by Microsoft Teams, and Facebook.

Kore.Ai features:

• AI/Machine Learning
• Business Process Automation
• Chatbot
• Code-free Development
• Contextual Guidance
• For Developers
• Intent Recognition
• Live Chat
• Multi-Channel Communication
• Multi-Language
• Natural Language Processing
• Personalization
• Pre-Configured Bot
• Reporting/Analytics
• Sentiment Analysis

4. Rasa

Rasa stands as the foremost conversational AI platform, designed to empower the creation of AI-enhanced, industrial-level chatbots tailored for multidisciplinary teams within enterprises.

Developers across the globe favor Rasa for the development of chatbots and contextual assistants, showcasing its global acceptance and application. Being a free and open-source platform, Rasa addresses the needs of businesses aiming to elevate subscriber engagement through personalized customization, marketing automation, and enhanced customer interaction.

With compatibility for Facebook Messenger, Rasa offers an advanced understanding of customer needs and behaviors. Businesses can also deploy Rasa on their servers, ensuring control over their components while interacting flexibly with users.

Rasa Features:

• AI/Machine Learning
• Contextual Guidance
• For Developers
• Intent Recognition
• Natural Language Processing
• Pre-Configured Bot
• Reporting/Analytics
• Third-Party Integrations

5. DialogFlow

Dialogflow is recognized as a leading natural language understanding platform that enhances user engagement through artificial intelligence. It facilitates the management of automated conversations by deploying its NLP technology.

With an offer of a free trial, Dialogflow seamlessly integrates a conversational user interface into various platforms such as mobile apps, web applications, devices, bots, or interactive voice response systems without any upfront charges.

Designing with Dialogflow is straightforward, leveraging Cloud speech-to-text technology for precise speech recognition. Thanks to its compatibility with over 400 million Google Assistant devices, Dialogflow stands out as a preferred choice for developing interactive actions.

Dialogflow features:

• Multi-Channel Communication
• Multi-Language
• Small Talk
• Multilingual Agent Support
• Cross Platform Support
• Fulfillment
• Training
• Agent Creation & Management
• Entities
• Intents
• Integrations
• In-Line Code Editor
• Analytics

Conclusion

Customers universally desire straightforward interactions, while businesses aim to utilize the finest chatbot tools to efficiently cater to these expectations. An AI chatbot stands out as the premier solution, handling a vast array of conversations around the clock and delivering effective outcomes.

The article highlighted five chatbot examples proficient in human-like discourse through the use of NLP, encompassing areas such as language learning, customer support, personal finance, and news. These examples underscore the transformative potential of NLP in crafting chatbots capable of comprehending and engaging in natural language dialogue.

With ongoing advancements in NLP technology, the expectations grows for more refined chatbots that can seamlessly interact in a human-like manner. The prospective developments in chatbot technology hold great promise, and there is considerable enthusiasm about their emerging applications to improve various aspects of daily life.

For those in search of a customer support chatbot, a tool for lead generation, or an e-commerce assistant, there are platforms designed to manage intricate interactions. These chatbots adapt and learn from every exchange, progressively enhancing their performance.

Frequently Asked Questions (FAQs)

How do you use NLP in Chatbots?

NLP in chatbots involves programming these digital assistants to comprehend and reply to human language accurately. This process uses algorithms to examine the input thoroughly, extract its meaning, and generate contextually relevant responses, facilitating more natural and human-like interactions.

What are the features of an NLP chatbot?

NLP-powered chatbots are equipped with advanced features such as sentiment analysis, entity recognition, and the ability to understand user intents. They are proficient in maintaining context, which leads to more coherent and human-like conversations. Moreover, these chatbots possess the capability to adjust to different linguistic styles, thereby improving user engagement.

Is NLP required for a chatbot?

Integrating Natural Language Processing (NLP) is not mandatory but significantly enhances chatbot performance. Chatbots without NLP may have difficulties understanding user inputs accurately and offering relevant responses. The inclusion of NLP fosters smoother and more effective interactions, rendering the chatbot experience more user-friendly and efficient.

Why is NLP good for chatbots?

NLP enhances chatbot capabilities by enabling them to understand and respond to user input in a more natural and contextually aware manner. This technology improves user satisfaction, reduces communication barriers, and broadens the range of queries chatbots can handle, making them indispensable for effective human-like interactions.