{"title":"Natural Language Processing Chatbot: NLP in a Nutshell","content":"<p><h1>What is an NLP chatbot, and do you ACTUALLY need one? RST Software</h1></p>\r\n<p><img class='wp-post-image' style='display: block;margin-left:auto;margin-right:auto;' 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width=\"309px\" alt=\"nlp for chatbots\"/></p>\r\n<p><p>Natural language understanding (NLU) is a subset of NLP that’s concerned with how well a chatbot uses deep learning to comprehend the meaning behind the words users are inputting. NLU is how accurately a tool takes the words it’s given and converts them into messages a chatbot can recognize. Natural language processing chatbots, or NLP chatbots,&nbsp; use complex algorithms to process large amounts of data and then perform a specific task.</p></p>\r\n<p><img class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"306px\" alt=\"nlp for chatbots\"/></p>\r\n<p><p>It then receives a reward from the user and moves on to the next state. The goal of the chatbot is to find the optimal policies and skills that maximize the rewards. I used 1000 epochs and obtained an accuracy of 98%, but even with 100 to 200 epochs you should get some pretty good results. The first step to creating the network is to create&nbsp;what in Keras is known as&nbsp;placeholders&nbsp;for the inputs, which in our case are the&nbsp;stories&nbsp;and the&nbsp;questions.</p></p>\r\n<p><h2>Improve your customer experience within minutes!</h2></p>\r\n<p><p>When the first few speech recognition systems were being created, IBM Shoebox was the first to get decent success with understanding and responding to a select few English words. Today, we have a number of successful examples which understand myriad languages and respond in the correct dialect and language as the human interacting with it. Chatbots with AI and NLP are equipped with a dialog model, which use intents and entities and context from your application to return the response to each user. The dialog is a logical flow that determines the responses your bot will give when certain intents and/or entities are detected. In other words, entities are objects the user wants to interact with and intents are something that the user wants to happen.</p></p>\r\n<p><p>Artificial intelligence is all set to bring desired changes in the business-consumer relationship scene. Twilio — Allows software developers to programmatically make and receive phone calls, send and receive text messages, and perform other communication functions using web service APIs. You can even offer additional instructions to relaunch the conversation. So, when logical, falling back upon rich elements such as buttons, carousels or quick replies won’t make your bot seem any less intelligent. To nail the NLU is more important than making the bot sound 110% human with impeccable NLG.</p></p>\r\n<p><h2>Key Components of an Intelligent Chatbot</h2></p>\r\n<p><p>Mr. Singh also has a passion for subjects that excite new-age customers, be it social media engagement, artificial intelligence, machine learning. He takes great pride in his learning-filled journey of adding value to the industry through consistent research, analysis, and sharing of customer-driven ideas. Traditional chatbots have some limitations and they are not fit for complex business tasks and operations across sales, support, and marketing. When you build a self-learning chatbot, you need to be ready to make continuous improvements and adaptations to user needs. If your company tends to receive questions around a limited number of topics, that are usually asked in just a few ways, then a simple rule-based chatbot might work for you.</p></p>\r\n<p><p>AI chatbots find applications in various platforms, including automated chat support and virtual assistants designed to assist with tasks like recommending songs or restaurants. Inversely, machine learning powered chatbots are trained to find similarities and relationships between several sentence and word structures. These chatbots don’t need to be explicitly programmed; they need specific patterns to understand the user and produce a response (e. g pattern recognition). Finally, the complexities of natural language processing techniques need to be understood. Natural language processing (NLP) was utilized to include for the most part mysterious corpora with the objective of improving phonetic examination and was hence improbable to raise ethical concerns.</p></p>\r\n<p><p>But for many companies, this technology is not powerful enough to keep up with the volume and variety of customer queries. This question can be matched with similar messages that customers might send in the future. The rule-based chatbot is taught how to respond to these questions — but the wording <a href=\"https://www.metadialog.com/blog/nlp-for-building-a-chatbot/\">nlp for chatbots</a> must be an exact match. Last but not least, Tidio provides comprehensive analytics to help you monitor your chatbot’s performance and customer satisfaction. For instance, you can see the engagement rates, how many users found the chatbot helpful, or how many queries your bot couldn’t answer.</p></p>\r\n<p><img class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"306px\" alt=\"nlp for chatbots\"/></p>\r\n<p><p>Some of the best chatbots with NLP are either very expensive or very difficult to learn. So we searched the web and pulled out three tools that are simple to use, don’t break the bank, and have top-notch functionalities. As many as 87% of shoppers state that chatbots are effective when resolving their support queries. This, on top of quick response times and 24/7 support, boosts customer satisfaction with your business. The process can be developed with a Markov Decision Process, where human users are the environment. At each step, the chatbot takes the current dialogue state as input and outputs a skill or a response based on the hierarchical dialogue policy.</p></p>\r\n<p><p>This is a popular solution for those who do not require complex and sophisticated technical solutions. You can sign up and check our range of tools for customer engagement and support. If the user isn’t sure whether or not the conversation has ended your bot might end up looking stupid or it will force you to work on further intents that would have otherwise been unnecessary. On the other hand, if the alternative means presenting the user with an excessive number of options at once, NLP chatbot can be useful.</p></p>\r\n<p><img class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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width=\"304px\" alt=\"nlp for chatbots\"/></p>\r\n<p><p>Today’s top tools evaluate their own automations, detecting which questions customers are asking most frequently and suggesting their own automated responses. All you have to do is refine and accept any recommendations, upgrading your customer experience in a single click. Here are the 7 features that put NLP chatbots in a class of their own and how each allows businesses to delight customers.</p></p>\r\n<p><h2>Audio Data</h2></p>\r\n<p><p>They employ natural language understanding in combination with generation techniques to converse in a way that feels like humans. Hierarchically, natural language processing is considered a subset of machine learning while NLP and ML both fall under the larger category of artificial intelligence. The editing panel of your individual Visitor Says nodes is where you’ll teach NLP to understand customer queries. The app makes it easy with ready-made query suggestions based on popular customer support requests. You can even switch between  different languages and use a chatbot with NLP in English, French, Spanish, and other languages.</p></p>\r\n<p><img class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"300px\" alt=\"nlp for chatbots\"/></p>\r\n<p><p>Don’t be scared if this is your first time&nbsp;implementing an NLP model; I will go through every step, and put a link to the code at the end. For the&nbsp;best learning experience, I suggest you first read the post, and then go through the code while glancing at the sections of the post that go along with it. The problem with the approach of pre-fed static content is that languages have an infinite number of variations in expressing a specific statement.</p></p>\r\n<p><p>The objective is to create a seamlessly interactive experience between humans and computers. NLP systems like translators, voice assistants, autocorrect, and chatbots attain this by comprehending a wide array of linguistic components such as context, semantics, and grammar. AI allows NLP chatbots to make quite the impression on day one, but they’ll only keep getting better over time thanks to their ability to self-learn. They can automatically track metrics like response times, resolution rates, and customer satisfaction scores and identify any areas for improvement. They use generative AI to create unique answers to every single question.</p></p>\r\n<p><p>” the bot could match an intent named get_order_info only if the context named pizza_selected exists. Smarter versions of chatbots are able to connect with older APIs in a business’s work environment and extract relevant information for its own use. Even though NLP chatbots today have become more or less independent, a good bot needs to have a module wherein the administrator can tap into the data it collected, and make adjustments if need be.</p></p>\r\n<p><div style='border: black dashed 1px;padding: 15px;'><h3>Chatbots and Virtual Assistants: Evolution, Design Principles, and Use-Cases - Medium</h3><p>Chatbots and Virtual Assistants: Evolution, Design Principles, and Use-Cases.</p><p>Posted: Wed, 27 Dec 2023 08:00:00 GMT [<a href='https://news.google.com/rss/articles/CBMiemh0dHBzOi8vbWVkaXVtLmNvbS9AYWFtaXJhZnRhYmNsb3VkL2NoYXRib3RzLWFuZC12aXJ0dWFsLWFzc2lzdGFudHMtZXZvbHV0aW9uLWRlc2lnbi1wcmluY2lwbGVzLWFuZC11c2UtY2FzZXMtOTE4MTI1ODliODUx0gEA?oc=5' rel=\"nofollow\">source</a>]</p></div></p>\r\n<p><p>At this stage of tech development, trying to do that would be a huge mistake rather than help. In fact, this technology can solve two of the most frustrating aspects of customer service, namely having to repeat yourself and being put on hold. In our example, a GPT-3.5 chatbot (trained on millions of websites) was  able to recognize that the user was actually asking for a song recommendation, not a weather report. Looking for a comprehensive and affordable SEO tool that can help you optimize your website, track your rankings, and analyze your competitors? SE Ranking is a cloud-based SEO suite that offers a range of features for different aspects... In today’s AI-driven world, everyone’s incorporating AI into workflows, from generating blog posts to creating presentations.</p></p>\r\n<p><ul><li>Check out our Machine Learning books category to see reviews of the best books in the field if you are so eager to learn you can’t even finish this article!</li><li>It’s vital because it ensures you understand and get value from what you bought, keeps you happy and staying on, and cuts down on people leaving by making an excellent first impression.</li><li>Theoretically, humans are programmed to understand and often even predict other people’s behavior using that complex set of information.</li><li>Reading tokens instead of entire words makes it easier for chatbots to recognize what a person is writing, even if misspellings or foreign languages are present.</li><li>For computers, understanding numbers is easier than understanding words and speech.</li></ul></p>\r\n<p><p>Tools such as Dialogflow, IBM Watson Assistant, and Microsoft Bot Framework offer pre-built models and integrations to facilitate development and deployment. As the topic suggests we are here to help you have a conversation with your AI today. To have a conversation with your AI, you need a few pre-trained tools which can help you build an AI chatbot system. In this article, we will guide you to combine speech recognition processes with an artificial intelligence algorithm. In this article, we will create an AI chatbot using Natural Language Processing (NLP) in Python.</p></p>\r\n<p><a href=\"https://www.metadialog.com/blog/nlp-for-building-a-chatbot/\"><figure><img 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alt='nlp for chatbots' class='aligncenter' style='display:block;margin-left:auto;margin-right:auto;' width='407px'/></figure></a></p>\r\n<p><p>There are uncountable ways a user can produce a statement to express an emotion. Researchers have worked long and hard to make the systems interpret the language of a human being. For example, if a user first asks about refund policies and then queries about product quality, the chatbot can combine these to provide a more comprehensive reply. Don’t worry — we’ve created a comprehensive guide to help businesses find the NLP chatbot that suits them best. Missouri Star witnessed a noted spike in customer demand, and agents were overwhelmed as they grappled with the rise in ticket traffic. Worried that a chatbot couldn’t recreate their unique brand voice, they were initially skeptical that a solution could satisfy their fiercely loyal customers.</p></p>","excerpt":"","status":"publish","date":"2025-05-14T15:15:20","slug":"natural-language-processing-chatbot-nlp-in-a-10","categories":[-1]}