Natural Language Annotation for Machine Learning

Chapter 4. Building Your Model and Specification

Now that you’ve defined your goal and collected a relevant dataset, you need to create the model for your task. But what do we mean by “model”? Basically, the model is the practical representation of your goal: a description of your task that defines the classifications and terms that are relevant to your project. You can also think of it as the aspects of your task that you want to capture within your dataset. These classifications can be represented by metadata, labels that are applied to the text of your corpus, and/or relationships between labels or metadata. In this chapter, we will address the following questions:

• The model is captured by a specification, or spec. But what does a spec look like?

• You have the goals for your annotation project. Where do you start? How do you turn a goal into a model?

• What form should your model take? Are there standardized ways to structure the phenomena?

• How do you take someone else’s standard and use it to create a specification?

• What do you do if there are no existing specifications, definitions, or standards for the kinds of phenomena you are trying to identify and model?

• How do you determine when a feature in your description is an element in the spec versus an attribute on an element?

The spec is the concrete representation of your model. So, whereas the model is an abstract idea of what information you want your annotation to capture, and the interpretation of that information, the spec turns ...