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NLU systems improve over time Measured by accuracy of response, the systems improve when they are repeatedly trained. The larger the date set that is fed to train them the better is the accuracy of the model since it has covered all test cases that exists in the real world. A learning system can never […]

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NLU aspect Getting the NLU aspect is key to designing a conversation experience for a bot. If your bot cannot understand a sentence or the underlying intent it will lead to a default or a very frustration experience for the user. The elements of Natural Language Understanding (NLU) is the next step in our journey […]

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The introduction of pattern matching, stemming and lemmatisation techniques to Generation 1 bots led to bots being more “intelligent”. While they still remained as more of lookup bots or pattern matching bots, the advances and introduction of algorithms that help improve the interaction experience helped bots mature more on its evolutionary roadmap. Let’s look into […]

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In the previous blog, we understood the advances in algorithmic science to improve accuracy of Natural Language Processing (NLP) constructs and how vector scoring could be improved. We trace the evolution path further in this blog for base elements of the network and how adding a memory for long term dependencies optimise it further. Evolution […]

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Social media is a great platform for publishing your job ads and looking for potential employees. This is due to the fact that billions of people use social media every day and the audience is enormous. Your only job is to create a recruitment strategy and find ways to manage it successfully. That includes boosting […]

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Engati has recently launched Contextual Conversation to enhance the way users talk to the chatbot. Here’s what the context feature is all about, why we have launched it and how it’s going to benefit the user.   What is context? Context allows the user to have an informal conversation with the chatbot using pronouns. The […]

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Natural Language Understanding or NLU is a subtopic within NLP that should focus on deriving the meaning of a sentence in a conversation with a chatbot, determine the context of the conversation, predict the intent of the user and obtain word level metadata. NLU should employ reading comprehension techniques to process text data to extract […]

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Now that we understand the essentials of the NLU mechanisms that a bot applies, let’s turn our attention to the Natural Language processing elements. NLP is a set of algorithms that help machine technology understand, interpret and process language which can be in the form of text or speech. Users will ask the bot arbitrary […]

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Bots use various elements of NLP, NLU and NLG. Think about these terms as a set of libraries with algorithms at the core that help you understand what a user is asking or saying, by breaking down the user input. Analysing it, interpreting it, processing it, understanding its meaning and then searching for the right […]

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Bots are of various types In essence, bots are small pieces of code that are programmed to do a particular task again and again. Old style bots were built based on a set of rules. They were replicators. Rule bots that did A when they came across B, C if they came across D. A […]

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