Summary
In this chapter, we covered the topic of selecting NLP applications that have a good chance of success with current NLP technology. Successful applications generally have input with specific, objective answers, have training data available, and handle (at most) a few languages.
Specifically, this chapter addressed a number of important questions. We learned how to identify problems that are the appropriate level of difficulty for the current state of the art of NLU technology. We also learned how to ensure that sufficient data is available for system development and how to estimate the costs of development and maintenance.
Learning how to evaluate the feasibility of different types of NLP applications as discussed in this chapter will be extremely valuable as you move forward with your NLP projects. Selecting an application that is too ambitious will result in frustration and a failed project, whereas selecting an application that is too easy for the state of the art...
