Combining Evidence with Logic and Preferences to Learn Relations from Structured Few Sparse Textual Data

In the literature, it is commonly believed that learning from few data problem can be resolved by using classifiers that consider interclass relationships. In this work, we will adopt this point of view in learning from few sparse textual data, essentially, by considering the sparseness of the latter as a good support for inducing theories about generalization. Therefore, we opt for an inductive approach based on combining: evidence-based analysis of patterns, logic and preferences. More precisely, we are interested in supervised learning of biomedical articles by exploiting a multi-scale hybrid description and constrained pattern-based data mining techniques. Unlike existing works, we will highlight the relevance of the absence/weakness of patterns and we will associate to their absence a semantic value compared to their presence. The main characteristic of our approach is that of considering local and global contexts, which connect textual data by introducing regret ratio measures and generalized exclusive patterns in order to avoid a crisp effect between the absence and presence of patterns. Experimental results show the effectiveness of our approach.