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AimGiven a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels.MethodWe constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome). We explored the use of various features based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF) for classification.ResultsFor the classification tasks over all labels, our systems achieved micro-averaged f-scores of 80.9% and 66.9% over datasets of structured and unstructured abstracts respectively, using sequential features. In labeling only the key sentences, our systems produced f-scores of 89.3% and 74.0% over structured and unstructured abstracts respectively, using the same sequential features. The results over an external dataset were lower (f-scores of 63.1% for all labels, and 83.8% for key sentences).ConclusionsOf the features we used, the best for classifying any given sentence in an abstract were based on unigrams, section headings, and sequential information from preceding sentences. These features resulted in improved performance over a simple bag-of-words approach, and outperformed feature sets used in previous work.

In this paper, we discuss forward responsibilities in Belief-Desire-Intention agents, that is, responsibilities that can drive future decision-making. We focus on individual rather than global notions of responsibility. Our contributions include: (a) extended operational semantics for responsibility-aware rational agents; (b) hierarchical responsibilities for improving intention selection based on the priorities (i.e., hierarchical level) of a responsibility; and (c) shared responsibilities which allow agents with the same responsibility to update their priority levels (and consequently commit or not to the responsibility) depending on the lack (or surplus) of agents that are currently engaged with it.