Java Reference
In-Depth Information
Selecting a model
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does well for determining the end of sentences for historical documents might not work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 permits us to select the appropriate model.
