## Purpose

Purpose of this document is to demonstrate the basic output one can expect after using extra-model.

## Data

The input looks like this:

As you can see, there are only 7 rows that all (essentially) repeat the same thing. The only difference is that each time the type of wood changes. This is done on purpose to demonstrate, what we think, a unique feature of extra-model: semantic grouping.

## Output

As a result of running extra-model, we get following output:

There are a lot of different columns, but for now we’ll only concentrate on following 3: Aspect, Topic, WordnetNode.

Notice how all of the different types of wood are mapped to the same WordNet node (wood.n.01). This is powerful since we went from completely unstructured data to the point where we know that 6 out of 7 comments are talking about different types of wood.

Moreover, we add “native” WordNet node (WordnetNode column) to the output. This gives us even richer output since not only did we group our comments, we’ve also enriched each of them with semantic meaning that can be used in any downstream tasks.

Finally, since extra-model is unsupervised, it is possible to have output that doesn’t always conform to what we would expect. Concretely, we would like wood in the first comment to also map to wood WordNet node. However, it was mapped to forest. Situations like these are possible when using extra-model, so we leave it here on purpose to make sure that you are aware of this possibility.