{"id":578,"date":"2019-12-05T04:40:57","date_gmt":"2019-12-05T04:40:57","guid":{"rendered":"https:\/\/www.danielparente.net\/en\/2019\/12\/05\/natural-language-processing-chatbot-explained\/"},"modified":"2019-12-05T04:40:57","modified_gmt":"2019-12-05T04:40:57","slug":"natural-language-processing-chatbot-explained","status":"publish","type":"post","link":"https:\/\/www.danielparente.net\/en\/2019\/12\/05\/natural-language-processing-chatbot-explained\/","title":{"rendered":"Natural Language Processing Chatbot Explained"},"content":{"rendered":"

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What is NLP? How does it work? How is it used? What are the workings and advantages of a natural language processing chatbot and do you actually need one? We break down this AI sub-domain with focus on its application in chatbot technologies in plain terms, to make the concept comprehensible and accessible to anyone \u2013 including non-techies.<\/em><\/span><\/p>\n\n

Chatbots, though they have been on the scene for quite a few years, are still a hot topic. Generally speaking, you get two types of approaches to their creation:<\/p>\n

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  1. Companies determined to develop bots capable of natural conversations indistinguishable from human<\/strong> ones using NLP and machine learning.<\/li>\n
  2. Companies focused on functional bots<\/strong> good at accomplishing specific tasks and do so quickly and efficiently, making the bots\u2019 perceived humanity a secondary matter.<\/strong><\/li>\n<\/ol>\n

    The issue is that with all the hype around artificial intelligence, it\u2019s often difficult to maintain perspective; distinguish fact from fiction; manage user expectations, or simply resist being seduced by technology for its own sake (rather than keeping your business\/marketing goal in mind).<\/p>\n

    Frankly, a chatbot doesn\u2019t necessarily need to fool you into thinking its human to be successful in completing its raison d\u2019\u00eatre<\/em>. At this stage of tech development, trying to do that would be a huge mistake rather than help<\/span>.<\/span><\/h6>\n

    In fact, while any talk of chatbots is usually accompanied by the mention of AI, machine learning and natural language processing (NLP), many highly efficient bots are pretty \u201cdumb\u201d and far from appearing human. Rule-based bots with option buttons, yes-no questions, keyword recognition, and quick answer suggestions seem to generate great results. Sometimes even better than NLP!<\/p>\n

    Nevertheless, this doesn\u2019t mean you should give up on it. Natural Language Processing does have an important role in the matrix of bot development<\/strong>. The key is a successful application of NLP is understanding how and when to use it.<\/p>\n

    To make the sense of it all, we broke down the essence NLP, its inner workings and possible uses.<\/p>\n\n

    Why Machines Need NLP?<\/h2>\n\n

    Everything we express in written or verbal form encompasses a huge amount of information that goes way beyond the meaning of individual words. The combination of topic, tone, selection of words, sentence structure, punctuation\/expressions allows humans to interpret that information, its value, and intent. Theoretically, humans are programmed to understand and often even predict other people\u2019s behavior using that complex set of information.<\/p>\n

    The problem?<\/p>\n

    One person can generate hundreds of words in a declaration, each sentence with its own complexity and contextual undertone. To scale bot\u2019s understanding means analyzing several hundred or thousands of people and their possible declarations which differ further from one geography to the next\u2026<\/p>\n

    This kind of conversational data<\/strong> is called unstructured data<\/strong> and can\u2019t be fit into neatly stacked rows and columns. It\u2019s complicated, messy and hard to manipulate. The only way to teach a machine about all that is to let it learn from experience.<\/p>\n\n

    What is NLP?<\/h2>\n\n

    Natural language<\/strong> is the language humans use to communicate with one another. On the other hand, programming language<\/strong> was developed so humans can tell machines what to do in a way machines can understand. For example, English is a natural language while Java is a programming one.<\/p>\n

    Natural Language Processing facilitates human-to-machine communication without humans needing to \u201cspeak\u201d Java or any other programming language <\/strong>as it allows machines to obtain and process information from written or verbal user inputs.<\/p>\n

    In essence, developers create NLP models that enable computers to decode and even mimic the way humans communicate.<\/p>\n

    Unlike common word processing operations, NLP doesn\u2019t treat speech or text just as a sequence of symbols. It also takes into consideration the hierarchical structure of the natural language<\/strong> \u2013 words create phrases; phrases form sentences;\u00a0 sentences turn into coherent ideas. In other words, NLP software doesn\u2019t just look for keywrods. It uses pre-programmed or acquired knowledge to decode meaning and intent from factors such as sentence structure, context, idioms, etc.<\/strong> For instance, good NLP software should be able to recognize whether the user\u2019s \u201cWhy not?\u201d indicates agreement or a question that requires an answer.<\/p>\n

    NLP is divided in two key categories:<\/p>\n

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    1. Natural Language Understanding (NLU)<\/li>\n
    2. Natural Language Generation (NLG)<\/li>\n<\/ol>\n\n

      How Does NLP Fit into the AI World?<\/h2>\n\n

      The words AI, NLP, and ML (machine learning)<\/a> are sometimes used almost interchangeably. However, there is an order to the madness of their relationship.<\/p>\n

      Hierarchically, natural language processing is considered a subset of machine learning while NLP and ML both fall under the larger category of artificial intelligence.<\/p>\n