Keyphrase extraction is a widely discussed topic in Natural Language Processing, as it offers a concise summary of the main topics in a document. Interpretability is also an important aspect in Machine Learning as it helps prevent socio-ethical issues, such as bias and discrimination against minorities, or mistakes that may have serious consequences. Interpretability has recently gained prominence in the field of Natural Language Processing, where transformers are the dominant architectures. The goal of interpretability is to provide interpretations that pinpoint the elements of an instance contributing the most to its decision. In this work, we use keyphrase extraction to facilitate the interpretability process, producing smaller, more concise interpretations that also consider word interactions, as keyphrases usually consist of multiple words. Additionally, our technique is based on semantic similarity, making it faster and zero-shot ready, which is ideal for online learning scenarios. We evaluated the effectiveness of our technique through a series of quantitative and qualitative experiments on the well-known BERT model, comparing it against several state-of-the-art competitors.

This work presents a probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships among labels. In particular we focus on discovering two kinds of relationships among the labels. The first one concerns pairwise positive entailment: pairs of labels, where the presence of one implies the presence of the other in all instances of a dataset. The second concerns exclusion: sets of labels that do not coexist in the same instances of the dataset. These relationships are represented as a deterministic Bayesian network. Marginal probabilities are entered as soft evidence in the network and through probabilistic inference become consistent with the discovered knowledge. Our approach offers robust improvements in mean average precision compared to the standard binary relevance approach across all 12 datasets involved in our experiments. The discovery process helps interesting implicit knowledge to emerge, which could be useful in itself.

A simple yet effective multilabel learning method, called label powerset (LP), considers each distinct combination of labels that exist in the training set as a different class value of a single-label classification task. The computational efficiency and predictive performance of LP is challenged by application domains with large number of labels and training examples. In these cases, the number of classes may become very large and at the same time many classes are associated with very few training examples. To deal with these problems, this paper proposes breaking the initial set of labels into a number of small random subsets, called labelsets and employing LP to train a corresponding classifier. The labelsets can be either disjoint or overlapping depending on which of two strategies is used to construct them. The proposed method is called RAkEL (RAndom k labELsets), where k is a parameter that specifies the size of the subsets. Empirical evidence indicates that RAkEL manages to improve substantially over LP, especially in domains with large number of labels and exhibits competitive performance against other high-performing multilabel learning methods.