of models which emphasize interpretability. We focus on models that were explicitly designed to be interpretable, that is, models where introducing or increasing the interpretability was a primary requirement driving the design choices. We divide the models into three categories: *data models*, *algorithmic models*, and *hybrid models*, which take into account the content where an interpretation is to be extracted from. These categories are not mutually exclusive: algorithmic models can have minor internal data representations and vice-versa; therefore for classifying the models we considered the most predominant characteristic of each of them.

Data Models
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Interpretable data models are models whose core component is preprocessed data, which goes through some clusterization process for posterior use. Although these models are usually built by some machine learning method, what define such models, rather than the construction procedure, is the shape and content of the final product, which will be used as input for a number of other tasks.

The most outstanding examples of such models are interpretable **Distributional Semantics Models (DSMs)**. These models are grounded in the distributional hypothesis, which states that words that occur in similar contexts tend to have similar meanings [@turney2010frequency], and allow words to be represented as a 