Many of the tasks that we will examine are based on models. For example, if we need to split a document into sentences, we need an algorithm to do this. However, even the best sentence-boundary-detection techniques have problems doing this correctly every time. This has resulted in the development of models that examine the elements of text and then use this information to determine where sentence breaks occur.

The right model can be dependent on the nature of the text being processed. A model that does well for determining the end of sentences for historical documents might not work well when applied to medical text.

Many models have been created that we can use for the NLP task at hand. Based on the problem that needs to be solved, we can make informed decisions as to which model is the best. In some situations, we might need to train a new model. These decisions frequently involve trade-offs between accuracy and speed. Understanding the problem domain and the required quality of results enables us to select the appropriate model.