Demystifying AI: What is Natural Language Processing NLP and How Does It Work?
In this encoding technique, the sentence is first tokenized into words. They are represented in the form of a list of unique tokens and, thus, vocabulary is created. This is then converted into a sparse matrix where each row is a sentence, and the number of columns is equivalent to the number of words in the vocabulary. NLP is used to summarize a corpus of data so that large bodies of text can be analyzed in a short period of time. Document summarization yields the most important and useful information.
This is also the stage where stop words (frequent, insignificant tokens) are removed as part of the semantic analysis. According to a survey done by McKinsey, companies that excel at personalisation generate 40% more revenue from those activities than average players. With this being said, personalisation is not something that customers just want; they demand it. To get started on creating NLP capability for your bot, go Create your NLP models here.
How are Chatbots Trained?
It breaks down your input into tokens or individual words, recognising that you are asking about the weather. Then, it performs syntactic analysis to understand the sentence structure and identify the role of each word. In a sentence of the type, I would like to purchase a year’s membership or I would like to book an appointment it is easy to identify the Intent, namely to purchase and to make a booking respectively.
And an Entity model which recognises locations and another that recognises ages. Your chatbots can then utilise all three to offer the user a purchase from a selection that takes into account the age and location of the customer. Natural language processing technology in conversational AI chatbots will help the bot replicate the human persona accurately by processing and understanding the language. NLP technology will process human language and enable bots to read and interpret text messages. Basic chatbots require that a user click on a button or prompt in the chatbot interface and then return the next part of the conversation. This kind of guided conversation, where a user is provided options to click on to progress down a specific branch of the conversation, is referred to as CI, or conversational interfacing.
NLP helps structure this data and makes it easy to understand the idea behind the customer reviews, comments, inputs, or queries. Chatbots can answer over 100 questions, while a human service advisor can only answer one. We use a variety of tools to build AI chatbots, including LUIS by Microsoft. There are many factors in which bots can vary, but one of the biggest differences is whether or not a bot is equipped with Natural Language Processing or NLP. Imagine you have a virtual assistant on your smartphone, and you ask it, “What’s the weather like today?” The NLP algorithm first goes through the understanding phase.
However, in the beginning, NLP chatbots are still learning and should be monitored carefully. It can take some time to make sure your bot understands your customers and provides the right responses. Natural Language Processing is a way for computer programs to converse with people in a language and format that people understand. The advent of NLP-based chatbots and voice assistants is revolutionising customer interaction, ushering in a new age of convenience and efficiency. This technology is not only enhancing the customer experience but also providing an array of benefits to businesses.
Chatbots can be used to simplify order management and send out notifications. Chatbots are interactive in nature, which facilitates a personalized experience for the customer. As it is the Christmas season the employees are busy helping customers in their offline store and have been busy trying to manage deliveries. But you don’t need to worry as they were smart enough to use NLP chatbot on their website and say they called it “Fairie”. Now you will click on Fairie and type “Hey I have a huge party this weekend and I need some lights”.
Chatbots have become more of a necessity now for companies big and small to scale their customer support and automate lead generation. Reduce costs and boost operational efficiency
Staffing a customer support center day and night is expensive. Likewise, time spent answering repetitive queries (and the training that is required to make those answers uniformly consistent) is also costly.
The Naive Bayes algorithm tries to categorize text into different groups so that the chatbot can determine the user’s purpose, range of possible responses. It is crucial that this algorithm functions well because intent identification is one of the first and most important phases in chatbot discussions. Because the algorithm is based on commonality, certain terms should be given greater weight for specific categories based on how frequently they appear in those categories.
Algorithms are used to reduce the number of classifiers and create a more manageable structure. With custom integrations, your chatbot can be integrated with your existing backend systems like CRM, database, payment apps, calendar, and many such tools, to enhance the capabilities of your chatbot. These are client-facing systems such as – Facebook Messenger, WhatsApp Business, Slack, Google Hangouts, your website or mobile app, etc.
They are based on deep learning techniques, which is a method of training a neural network using a large dataset. Simply put, a chatbot is a program that engages in conversations with humans using Artificial Intelligence (AI) technologies such as Natural Language Understanding (NLU) and Machine Learning. Think of an AI chatbot as a virtual assistant that you can talk with in a two-way dialogue. It can understand human language, interpret your questions and respond to them in a meaningful way. To keep up with consumer expectations, businesses are increasingly focusing on developing indistinguishable chatbots from humans using natural language processing.
As the MIT Technology Review explains, this latest version is capable of explaining the humor behind memes or even creating a recipe based on pictures of food items. Businesses use massive quantities of unstructured, text-heavy data and need a way to efficiently process it. A lot of the information created online and stored in databases is natural human language, and until recently, businesses could not effectively analyze this data.
💬 Frequently Asked Questions About Natural Language Processing
Language is a bit complex (especially when you’re talking about English), so it’s not clear whether we’ll ever be able train or teach machines all the nuances of human speech and communication. 4) Input into NLP Platform- (NLP Training) Once intents and entities have been determined and categorized, the next step is to input all this data into the NLP platform accordingly. Training starts at a certain level of accuracy, based on how good training data is, and over time you improve accuracy based on reinforcement. During training you might tell the new Home Depot hire that “these types of questions relate to pricing requests”, or “these questions are relating to the soil types we have”. A vast majority of these requests will fall into different buckets, or “intents”.
Find critical answers and insights from your business data using AI-powered enterprise search technology. Both of these processes are trained by considering the rules of the language, including morphology, lexicons, syntax, and semantics. This enables them to make appropriate choices on how to process the data or phrase responses. In the process of writing the above sentence, I was involved in Natural Language Generation. Let’s look at how exactly these NLP chatbots are working underneath the hood through a simple example.
Machine learning chatbots, on the other hand, are still in primary school and should be closely controlled at the beginning. NLP is prone to prejudice and inaccuracy, and it can learn to talk in an objectionable way. NLP chatbots are able to interpret more complex language which means they can handle a wider range of support issues rather than sending them to the support team. This augments the support team allowing it to run smoother and on a tighter budget. One of the most significant challenges when it comes to chatbots is the fact that users have a blank palette regarding what they can say to the chatbot. While you can try to predict what users will and will not say, there are bound to be conversations that you would never imagine in your wildest dreams.
Hence, NLP technology is the best way to understand user intent and develop the business around it. Without Natural Language Processing, a chatbot can’t meaningfully differentiate between the responses “Hello” and “Goodbye”. To a chatbot without NLP, “Hello” and “Goodbye” will both be nothing more than text-based user inputs. Natural Language Processing (NLP) helps provide context and meaning to text-based user inputs so that AI can come up with the best response.
- Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time.
- Once satisfied with your chatbot’s performance, it’s time to deploy it for real-world use.
- Research suggests that more than 57% of data scientists used python for Machine Learning.
- But, the more familiar consumers become with chatbots, the more they expect from them.
- In this method of embedding, the neural network model iterates over each word in a sentence and tries to predict its neighbor.
However, despite the compelling benefits, the buzz surrounding NLP-powered chatbots has also sparked a series of critical questions that businesses must address. Older chatbots may need weeks or months to go live, but NLP chatbots can go live in minutes. By tapping into your knowledge base — and actually understanding it — NLP platforms can quickly learn answers to your company’s top questions.
Read more about What is NLP Chatbot and How It Works? here.