In this chapter, we will address several issues surrounding the use of combinations of techniques to solve NLP problems. We start with a brief introduction to the process of preparing data. This is followed by a discussion on pipelines and their construction. A pipeline is nothing more than a sequence of tasks integrated to solve some problems. The chief advantage of a pipeline is the ability to insert and remove various elements of the pipeline to solve a problem in a slightly different manner.

The Stanford API supports a good pipeline architecture, which we have used repeatedly in this book. We will expand upon the details of this approach and then show how OpenNLP can be used to construct a pipeline.

Preparing data for processing is an important first step in solving many NLP problems. We introduced the data preparation process in Chapter 1, Introduction to NLP, and then discussed the normalization process in Chapter 2, Finding Parts of Text. In this chapter...