Word sense embeddings represent a word sense as a low-dimensional numeric vector. While this representation is potentially useful for NLP applications, its interpretability is inherently limited. We propose a simple technique that improves interpretability of sense vectors by mapping them to synsets of a lexical resource. Our experiments with AdaGram sense embeddings and BabelNet synsets show t...

Models work best when they are optimized taking into account the evaluation criteria that people care about. For topic models, people often care about interpretability, which can be approximated using measures of lexical association. We integrate lexical association into topic optimization using tree priors, which provide a flexible framework that can take advantage of both first order word ass...

A fuzzy rule-based system design concentrates on accuracy and interpretability of the system. Fuzzy decision tree method is proposed based on fuzzy RDBMS and rule generation based on C4.5 algorithm known as fuzzy rule generation system (FRGS) algorithm. A fuzzy decision tree is developed by first converting a medical application of heart relational database to fuzzy heart relational database an...

Improving the interpretability of multivariate models is of primary interest for many neuroimaging studies. In this study, we present an application of multi-task learning (MTL) to enhance the interpretability of linear classifiers once applied to neuroimaging data. To attain our goal, we propose to divide the data into spatial fractions and define the temporal data of each spatial unit as a ta...

Rule based systems are basically knowledge base systems. They imitate the functionalty of human decision making process in machines. The knowledge is stored in the Knowledge Base of the fuzzy rule based systems. The knowledge is expressed, manipulated and processed by using fuzzy logic. Fuzzy logic represents the human knowledge very well because its mathematical framework is very strong to dea...

We further investigate the relationship between accuracy and interpretability during the design of fuzzy systems. Both aspects are of major importance for the control of neuromuscular blockade. After describing how these goals can be measured, a multi-objective evolutionary optimization scheme is set up. The results show that even for the best optimization runs at the accuracy side of the Paret...

In computational biology, the analysis of high-throughput data poses several issues on the reliability, reproducibility and interpretability of the results. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to i...

Interpretability and accuracy are two important features of fuzzy systems which are conflicting in their nature. One can be improved at the cost of the other and this situation is identified as “Interpretability-Accuracy Trade-Off”. To deal with this trade-off Multi-Objective Evolutionary Algorithms (MOEA) are frequently applied in the design of fuzzy systems. Several novel MOEA have been propo...

The prime consideration in designing sustainable language resources is to ensure that they remain interpretable for coming generations of users. In this paper we adopt a new perspective on resource creation securing the interpretability of data, using a case study of Ega, an endangered African language for which a small amount of legacy data is available. Basic steps to securing interpretabilit...