NLP For Chatbot

NLP For Chatbot
Imagine if a computer did part of your work with customers. Some intelligent computers can work as information on your site or answer customer calls… Fascinating, isn’t it? But this is no longer a fantasy but reality: modern chatbots using NLP are no longer different from people. And this is thanks to the introduction of natural language processing into bot software. Natural language processing may greatly facilitate our daily lives and businesses. In this article, we will speak about bringing your bot to life with the help of NLP tools.

Some words about NLP

NLP stands for natural language processing. You can use NLP technology to help the machine understand human speech and spoken words. NLP for chatbot combines computational linguistics, i.e., rule-based modeling of human spoken language, with intelligent mechanisms such as statistical, machine, and deep learning algorithms. Together, these technologies create intelligent voice assistants and bots that you may use daily. People use several human errors, differences, and particular intonations daily in their speech. NLP technology allows a machine to understand, process, and quickly respond to large amounts of text in real-time. In daily life, you have come across NLP technology in voice-guided GPS apps, virtual assistants, speech-to-text note-taking apps, and other app-enabled bots. This technology has many applications in the business world, where it is used to streamline processes, monitor employee performance, and improve sales and after-sales performance.
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How does NLP work?

One of the basic principles of working with NLP is to break down a complex language system into atomic ones and teach the machine how to interact with them. The ability of a computer to work with human language includes the following aspects:
  • Natural language understanding (NLU): the central part of the interaction of a machine with human language is the ability to understand it. Language is a highly unstructured phenomenon governed by flexible rules (not to mention abbreviations, slang, misspellings, and accents). The text is invested with different emotions and meanings. For a machine to work and understand such data, human language must be converted into a logical form that the computer’s algorithms may realize.
  • Natural language generation (NLG): after structuring the data and parsing its meaning, the machine must turn it into a written narrative, generating readable text. With NLU and NLG, you can fully automate data-driven storytelling, generating financial reports, analyzing statistics, and more.
  • Natural Language Interaction (NLI) is the result of the above steps. A machine can interact with people in their native language if it can analyze human speech and generate human-like text. To be understood by a computer, we don’t need to give them commands using programming algorithms – enter data in our language. In turn, the computer will answer a form understandable to us.
One of the best things about NLP for chatbot is that it is probably the most straightforward element of AI to explain to non-technical people. Take the prediction algorithm in your email, one of the most common examples of natural language processing applications. The software doesn’t just guess what you want to say next, but it analyzes the likelihood of it based on tone and topic. Engineers may do this by providing a computer and «NLP training».

Why should we use NLP when building bots?

NLP is a tool for computers to analyze, understand and extract meaning from natural language reasonably and helpfully. It goes far beyond the newly developed chatbots and intelligent virtual assistants. Natural language processing algorithms are everywhere: search, online translation, spam filters, and spell checking. Thus, with the help of NLP, developers can organize and structure a mass of unstructured data to perform tasks such as intelligence:
  • Auto summation: intelligent reduction of long text fragments.
  • Automatic suggestions: used to speed up the writing of emails, messages, and other texts.
  • Translation: translating phrases and ideas instead of word for word.
  • Named object recognition: used to find and classify named objects in unstructured natural languages ​​into predefined categories.
  • Relationship extraction (extraction of semantic relationships between identified entities in natural language text/speech, such as «is located in», «is employed by», and more).
  • Sentiment analysis: helps to identify positive, negative, and neutral opinions in the form of text or speech, widely used to obtain information from comments on social networks or forums.
  • Speech recognition: allows computers to recognize and convert spoken language into text – dictation – and, if programmed, act according to this recognition – relevant for assistants such as Google Assistant Cortana or Apple Siri.
  • Topic segmentation: automatically divides written texts, speech, or recordings into shorter, thematically related segments and is used to improve information retrieval.
The work of any bot occurs in five main steps, including tokenization, normalization, intent recognition, dependency analysis, and generation. Such actions allow the bot to read, interpret, understand, formulate and send a response. NLP For Chatbot

Critical challenges for your AI chatbot

It’s the 21st century when computers are not just colossal computing machines. Modern computers can understand natural speech and respond to it. Through NLP, we may communicate. But human language is chaotic, despite its structure. So how exactly does natural language processing work? There are many elements in our speech that affect understanding by a natural language processing chatbot and can become problems in natural language processing:
  • synonyms, homonyms, slang,
  • spelling mistakes,
  • abbreviations,
  • skipping punctuation rules,
  • different accents.
People may understand the meaning of context, intonation, body language, and experiences. We can understand how a natural language processing chatbot works: since the machine does not have this linguistic experience, NLP involves teaching the computer to understand speech despite distractions.

How to build a chatbot using NLP?

Chatbots are a relatively new concept, and despite the vast number of NLP programs and tools, we only have two different groups of chatbots based on the NLP technology they use:
  • Scripted bots: Scripted bots are classified as chatbots that work with predefined scripts created and stored in their library. Whenever a user enters a query or speaks a request (in the case of chatbots equipped with speech-to-text modules), the chatbot responds to that query according to a predefined scenario.
  • Chatbots with artificial intelligence. As the name suggests, AI chatbots are designed to mimic human qualities and reactions. NLP or natural language processing is mainly responsible for chatbots understanding the dialects and nuances of human conversation. NLP combined with artificial intelligence creates a brilliant bot that may answer subtle questions and learn from every interaction to create better responses next time.
Creating an NLP-based bot is not an easy task. Let’s discuss the steps you need to follow to get a reliable and conversational assistant.

Business logic analysis

Such a step is necessary so the development team can understand our customers’ needs. To analyze the business logic, the team usually needs to conduct a discovery phase, study the competitive market, determine the main functions of your future bot, and finally create the business logic of your future product.

Channel and technology stack

If you plan to build a voice bot, it’s better to use the Twilio platform as your base channel. On the other hand, when building text bots, Telegram, Viber, or Hangouts are the proper channels to work with. Regarding the technology stack to develop chatbots, the most popular and frequently used technologies are:
  • Python is a famous programming language used to build the architecture of your future chatbot.
  • Pandas is a software library for the Python programming language for data processing and analysis.
  • Twilio lets software developers make and receive phone calls, send and receive text messages, and perform other communication functions through its web service APIs.
  • TensorFlow is a library that is often used for machine learning tasks and neural networks.
  • SpaCy is an open-source software library for advanced natural language processing.
  • Telegram, Viber, or Hangouts API – to connect a chatbot to your messengers or websites.
Each developer can independently choose the appropriate tools to create chatbots, considering the specifics of the business, the necessary functionality, and user needs.

Development and integration of NLP

Creating a machine learning chatbot involves two steps: developing the bot on the client side and connecting it to the provider’s API (Telegram, Viber, Twilio, etc.). Once the development is completed, you may add NLP to bots by connecting artificial intelligence.
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Once the bot is ready, we start asking questions we have taught the chatbot to answer. As usual, there are not many scripts to test so we can use manual testing. Testing helps determine if your AI NLP chatbot is working correctly. Chatbots with artificial intelligence can attract more users, save time and increase the status of your website. Therefore, the more users will be attracted to your site, the greater your profit will be. The field of chatbots is still challenging in terms of improving responses and choosing the best model that creates the most relevant answer based on the question, among other things. One of the most striking parts of intelligent bots is getting smarter with every meeting. On the other hand, machine learning chatbots are still in elementary school and should be carefully supervised initially. NLP for chatbot is prone to bias and inaccuracy and can be used to learn to speak inappropriately.
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