Natural Language Understanding with Python

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Identifying Practical Natural Language Understanding Problems

In this chapter, you will learn how to identify natural language understanding (NLU) problems that are a good fit for today’s technology. That means they will not be too difficult for the state-of-the-art NLU approaches but neither can they be addressed by simple, non-NLU approaches. Practical NLU problems also require sufficient training data. Without sufficient training data, the resulting NLU system will perform poorly. The benefits of an NLU system also must justify its development and maintenance costs. While many of these considerations are things that project managers should think about, they also apply to students who are looking for class projects or thesis topics.

Before ...