within that document, while *word2vecAvg* computes the mean vector of them.

The doc2vec model promotes document-level embeddings for texts, and it is based on word embeddings [@mikolov:2013:word2vec], a preliminary work that assigns vector representations for words in order to capture their semantic relationships. *doc2vecApnews* stands for a 300-dimensional *doc2vec* [@le:2014:doc2vec] model, pre-trained over the Associated Press News textual dataset, of about 25M news articles from 2009 to 2015.

From the descriptors, rankers are defined as tuples of (descriptor, comparator), where the comparator corresponds to a dissimilarity function. We compose a ranker for each descriptor by choosing an appropriate comparator. Given that our method works on top of rankers, we have to define dissimilarity functions to be used along with those descriptors that is not explicitly associated with one. This is the case for the four textual descriptors adopted in UW, as well as the descriptors adopted in ME17-DIRSM. All remaining descriptors define their own comparators.

For the textual descriptors BoW and 2grams, we adopt the Weighted Jaccard distance, defined as $1 - J(\mathbf{u}, \mathbf{v})$, where $J$ is the Ruzicka similarity metric (Equation \[eq:Jaccard\]). Jaccard is a well-known and widely-used comparison metric for classic textual descriptors, specially for short texts, as 