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So we have now discussed how the NLP engine in a bot works. It’s time to tackle intelligent paths, Business platforms are primarily transaction driven, till we shift the model to open conversations that are random as humans will communicate with each other. This is typically switching contexts while having a conversation with another human, […]

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So what exactly is stemming and lemmatization and how does it get used in machine learning? The specific issues that these approaches solve for inflections in language use so that search / retrieval and response accuracy can be increased further. Stemming When we stem a branch we cut off the redundant branches to retain the […]

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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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Ed Lawler and John Bordeau of the Centre of Effective Organisations have reported that 50% of an HR department’s time is spent processing information and answering employee questions. This is indicative of the old-age technology and methods being used by HR departments currently, which cost money, time, and human efforts for the company. Clearly, it’s […]

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