# Making a labelled training data set

We are developing a classification system, where the categories are fixed, but many of them are inter-related.

For example, we have a category called, "roads" and another one called "traffic". We believe that the model will be confused by the text samples, which could be in roads category and also in traffic.

Some of our text samples are suitable for multi class labelling too. For example, "There is a garbage dump near the footpath. The footpath is broken completely". This text could be categorized into garbage bucket or footpath bucket.

We are going to build a training set for this classifier, by manually annotating the text. So, can we put multiple labels for one issue? How should we deal with text with multiple labels for it? Should they be added into all categories to which it is tagged to, as training sample ?

For example, "There is a garbage dump near the footpath. The footpath is broken completely". This text could be categorized into garbage bucket or footpath bucket. So, should this text be added as a training sample for garbage and footpath? How should we consider the labels?

• Are your categories being used as inputs to some other model, or are they the output? – j.a.gartner Jun 1 '15 at 17:01

Either way, label the classes as you would want your trained model to predict them. If you expect your classifier to predict an example is in both garbage and footpath, then you should label such an example with both. If you want it to disambiguate between them, then label with a single correct class.