monthly archives: November 2018

To give some pretext of my background I come from a programming background. When a program does not give the desired result I could ‘debug the program’. I could add breakpoints in the code, watch variables as they change, stop the program at a breakpoint and inspect the ‘state’ so to say and figure out […]

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With the advent of chatbots, training computers to read, understand and write language has become a big business. The training may seem easy at first but as you start your journey with Natural Language Processing (NLP) you realize that surmounting the challenges is no easy task. That’s why sentence similarity is amongst the toughest problems. […]

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There are many existing out-of-box NLP models available today. These can take a sentence and match it with a list of available sentences and pick the top matches from them. However, they make basic assumptions about the sentence structure or words in them. Those may not apply necessarily to a chatbot conversation. Let’s find out […]

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Conversations between a human and a chatbot have a distinct style. Typically humans use short phrases, short sentences, sometimes just a single word, question like sentence, with or without proper context and lowercase in most cases when seeking answers from a chatbot. This is where we talk about NLP implementations in chatbots. NLP implementations in […]

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Blockchain technology has now become the talk of the town on its own merit instead of just being known as the technology behind the massively popular cryptocurrency – Bitcoin. Blockchain has the promise of revolutionising many industries, completely changing the way in which systems operate. The top minds are surely keeping a close watch on […]

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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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