Set labelling using multi-label classification

We propose the task of set labelling. Starting from some examples members of a set, set labelling tries to infer the most appropriate labels for the given set. For this work, we consider sets of words. We illustrate the task and a possible solution with an application to the classification of cosmetic products and hotels. The novel solution proposed in this research is to incorporate a multi-label classifier trained from the labeled datasets. We use vectorization of the description of the seeds as input to the classifier as well as labels assigned to it. Given a previously unseen data, the trained classifier returns a ranked list of candidate labels (i.e., additional seeds) for the set. These results could then be used to infer the labels for the set. We implement our proposed solution to the classification of cosmetic products and hotels. We show that the solution is effective and efficient.