Chatbots: Essential Functionalities for Optimal Understanding

NLP machine learning chatbot

You’re interested in implementing a chatbot to improve user satisfaction and increase productivity, but you don’t know which software to choose among all the publishers on the market… You’re concerned about overly limited FAQs, or chatbots that don’t understand what’s being asked of them… Sound familiar?

The level of success for a conversational robot mainly depends on the quality of content, and the bot’s ability to understand questions and retrieve the right answers from the knowledge base. This article provides advice about which technology and functionalities to prioritise, in order to make your chatbot project a success.

The NLP Algorithm, your Chatbot’s Engine

The technology behind chatbots, called natural language processing, is essential for it to work properly. This technology is based on an NLP algorithm (Natural Language Processing), that identifies the user’s question and matches it with the suitable answer.

There are several natural language understanding methods – distance calculation, syntactic analysis, and keyword matching. To find out more about how natural language processing works and the benefits of each method, read this article. 

All technologies are not equal, and it is important to find out which method is used, the algorithm and matcher’s performance (by testing), the connection with external matchers, and to identify whether the publisher owns the technology or uses a third party software.

Essential Functionalities

  • Politeness and Small Talk

Your bot should be able to hold a smooth and natural conversation with your users. Like a human, it should therefore understand and use key phrases (hello, please, thank you, etc.) and small talk (compliments, jokes, etc.). To save time and to avoid creating it yourself, ensure that your bot’s knowledge base includes a social base.

NOA, the Paris and Ile-de-France Préfecture chatbot :

joke nlp chatbot dydu
  • Formulations

There are lots of different ways to ask the same question. You chatbot needs to be able to understand them all. Matching groups (groups of words and phrases that mean the same thing) and formulations allow to add these different words and phrases under a same knowledge article.

  • Activity Monitoring and Improvements

You’ve created and tested your knowledge base and deployed your bot on the channel of your choice. But that’s not the end. It’s important to regularly monitor your bot’s activity, to continually improve it.

There are several functionalities to help you with this. Firstly, analytics, which indicate, among other things, the quality of your interactions and dialogues (the bot’s level of understanding of the questions and its ability to provide the right answers) and user satisfaction with regards to the answer given. Quality alerts are also useful to check the quality of content according to predetermined and customisable criteria (broken links, long answer time, etc. ). Other functionalities can help continually improve your base. Analysis of failed dialogues or misunderstood sentences enables you to add new phrases to an existing knowledge article, or to create a new one. You can also identify similar knowledge articles in your base and choose to group them together or make them more distinguishable. 

Are you interested in creating a chatbot? Feel free to contact us

communication officer
Lucie Choulet
Communications officer