Modern chatbots are stylish and sophisticated. In fact, they can even feel human thanks to machine learning technology. To offer a better user experience, these AI-powered chatbots use a branch of AI known as natural language processing (NLP). These NLP chatbots, also known as virtual agents or intelligent virtual assistants, support human agents by handling time-consuming and repetitive communications. As a result, the human agent is free to focus on more complex cases and call for human input.
To keep up with consumer expectations, businesses are increasingly focusing on developing indistinguishable chatbots from humans using natural language processing. According to a recent estimate, the global conversational AI market will be worth $14 billion by 2025, growing at a 22% CAGR (as per a study by Deloitte). Guess what, NLP acts at the forefront of building such conversational chatbots.
What is NLP?
Natural Language Processing is referred to as NLP. It is a branch of artificial intelligence that assists computers in reading and comprehending natural human language. Its primary goal is to improve human-machine interaction.
You can assist a machine in comprehending spoken language and human speech by using NLP technology. NLP combines intelligent algorithms like a statistical, machine, and deep learning algorithms with computational linguistics, which is the rule-based modeling of spoken human language. NLP technology enables machines to comprehend, process, and respond to large amounts of text in real time. Simply put, NLP is an applied AI program that aids your chatbot in analyzing and comprehending the natural human language used to communicate with your customers.
How Does NLP Fit in the World of Chatbot Development
NLP enables chatbot developers to carry out tasks like intelligent tasks like:
Automatic recommendations – used to speed up the writing of emails, messages, and other texts
Translation – translating phrases and ideas instead of word for word
Recognition of named entities – used to locate and classify named entities in unstructured natural languages into pre-defined categories such as organizations, persons, locations, codes, and quantities.
Relationship extraction– The process of extracting the semantic relationships between the entities that have been identified in natural language text or speech.
Sentimental Analysis – helps identify, for instance, positive, negative, and neutral opinions from text or speech widely used to gain insights from social media comments, forums, or survey responses.
Speech recognition – allows computers to recognize the spoken language, convert it to text (dictation), and, if programmed, take action on that recognition.
Topical division – automatically divides written texts, speech, or recordings into shorter, topically coherent segments and is used in improving information retrieval or speech recognition.
Operation of Chatbots using key NLP techniques
It’s time to really delve into the details of how modern NLP-based chatbots operate. Tokenizing, normalizing, identifying entities, dependency parsing, and generation are the five main steps required for the chatbot to read, interpret, understand, formulate and send a response. Let’s look more closely in order:
Tokenizing: The chatbot begins by slicing text into small pieces (also known as “tokens”) and removing punctuation.
Normalizing: The bot then eliminates irrelevant information and changes words to their “normal” counterparts, such as by making everything lowercase.
Identifying Entities: Now that all of the words have been normalized, the chatbot tries to determine what kind of thing is being discussed.
Dependency Parsing: In the following step, the bot determines the function of each word in the sentence, such as noun, verb, adjective, or object
Generation: Finally, the chatbot generates a number of responses based on the information gathered in the previous steps and chooses the best one to send to the user.
Ways to consider and build NLP Chatbots
Logic in Business Analysis:
This step is necessary so that the development team can comprehend the requirements of our client. A team typically needs to conduct a discovery phase, research the competitive market, choose the key features of your future chatbot, and then develop the business logic of your future product before they can analyze business logic.
Right Tech Stack:
The following technologies are the most popular and widely used in the chatbot development tech stack:
- Python is a programming language that will be used to create the architecture of your future chatbot.
- Pandas is a data manipulation and analysis software library written for the Python programming language.
- TensorFlowis a popular library for machine learning and neural network tasks.
- SpaCy is an open-source natural language processing software library.
- APIs to connect your chatbot to your messengers or websites.
Development & NLP Integration:
The creation of the machine learning chatbot consists of two steps: the development of a client-side bot and connecting it to the provider’s API (Telegram, Viber, Twilio, etc.). Once we are done with the development, we can add NLP to chatbots by connecting artificial intelligence.
Testing:
Once the bot is ready, we start asking the questions that we taught the chatbot to answer. As usual, there are not that many scenarios to be checked so we can use manual testing. Testing helps to determine whether your AI NLP chatbot works properly.
NLP Chatbots – Possible Without Coding?
Unfortunately, a no-code natural language processing chatbot remains a pipe dream. You must create the classification system and train the bot to understand and respond in human-friendly ways. Integration with NLP required coding. However, you create simple conversational chatbots with ease by using Chat360 using a simple drag-and-drop builder mechanism.
Why Do you Have To Integrate Your Chatbots with NLP?
Natural Conversation Enabled by Intelligent Comprehension:
It is possible to establish a link between incoming human text and the system-generated response using NLP. This response can range from a simple answer to a query to an action based on a customer request or the storage of any information from the customer in the system database.
Chatbots built on NLP are intelligent enough to comprehend speech patterns, text structures, and language semantics. As a result, it gives you the ability to understandably analyze a large amount of unstructured data. Because NLP can comprehend morphemes from different languages, it enhances a boat’s ability to comprehend subtleties. NLP enables chatbots to comprehend and interpret slang, continuously learn abbreviations, and comprehend a range of emotions through sentiment analysis.
Perform Tedious Tasks with Ease:
In order for an organization to function, many different roles and resources are deployed; however, this entails the repetition of manual tasks across various verticals such as customer service, human resources, catalog management, or invoice processing. NLP-based chatbots dramatically reduce human efforts in operations such as customer service or invoice processing, requiring fewer resources while increasing employee efficiency. Employees can now focus on mission-critical tasks and tasks that positively impact the business in a far more creative manner, rather than wasting time on tedious repetitive tasks every day.
Customer Satisfaction:
Millennials today expect instant responses and solutions to their questions. NLP enables chatbots to understand, analyze, and prioritize questions based on their complexity, allowing bots to respond to customer queries faster than a human. Faster responses aid in the development of customer trust and, as a result, more business.
When you use chatbots, you will see an increase in customer retention. It reduces 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 important and satisfied.
Analysis for Improvement:
NLP assists in structuring unstructured content and extracting meaning from it. You can quickly grasp the meaning or concept underlying the customer reviews, inputs, comments, or queries. You can get a sense of how the user feels about your products or services.
Turn to NLP-based Chatbots
The future of chatbots is promising alright. It’s amazing how intelligent chatbots can be if you take the time to feed them the data they require to evolve and make a difference in your business. Remember, NLP just makes the whole interaction process smarter.
NLP-based chatbots can help you improve your business processes and elevate your customer experience while also increasing overall growth and profitability. It gives you technological advantages to stay competitive in the market by saving you time, effort, and money, which leads to increased customer satisfaction and engagement in your business. So it is always right to integrate your chatbots with NLP with the right set of developers.
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