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Natural Language Processing with TensorFlow
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NLP has a multitude of real-world applications. A good NLP system is that which performs many NLP tasks. When you search for today's weather on Google or use Google Translate to find out how to say, "How are you?" in French, you rely on a subset of such tasks in NLP. We will list some of the most ubiquitous tasks here, and this book covers most of these tasks:
Finally, to develop a system that can assist a human in day-to-day tasks (for example, VA or a chatbot) many of these tasks need to be performed together. As we saw in the previous example where the user asks, "Can you show me a good Italian restaurant nearby?" several different NLP tasks, such as speech-to-text conversion, semantic and sentiment analyses, question answering, and machine translation, need to be completed. In Figure 1.1, we provide a hierarchical taxonomy of different NLP tasks categorized into several different types. We first have two broad categories: analysis (analyzing existing text) and generation (generating new text) tasks. Then we divide analysis into three different categories: syntactic (language structure-based tasks), semantic (meaning-based tasks), and pragmatic (open problems difficult to solve):
Figure 1.1: A taxonomy of the popular tasks of NLP categorized under broader categories
Having understood the various tasks in NLP, let us now move on to understand how we can solve these tasks with the help of machines.