Paternalism by and towards groups

In many or most instances of paternalism, more than one person acts paternalistically, or more than one person is treated paternalistically. This chapter discusses some complications that arise in such group cases, which are largely ignored in the conceptual debate. First, a group of people who together perform an action may do so for different reasons, which makes it more challenging to determine whether the action is paternalistic. This gives us some reason not to pin the property of being paternalistic on actions, since we may alternatively pin it on reasons for actions and allow that these differ between members in the group. Second, the prevention of harmful consensual interactions is sometimes paternalism towards both or all involved, but only if all benefit from interference with themselves rather than with other members in the group, or if all want the harm or risk (more or less) for its own sake. Third, interrelations between three components of paternalism - interference, benevolence and consent - gives us reason to allow that an action can be paternalistic towards some but not others of those affected. This makes it even more difficult, and less relevant, to determine whether or not actions are paternalistic

Towards Semantic Modeling of Contradictions and Disagreements: A Case Study of Medical Guidelines

We introduce a formal distinction between contradictions and disagreements in natural language texts, motivated by the need to formally reason about contradictory medical guidelines. This is a novel and potentially very useful distinction, and has not been discussed so far in NLP and logic. We also describe a NLP system capable of automated finding contradictory medical guidelines; the system uses a combination of text analysis and information retrieval modules. We also report positive evaluation results on a small corpus of contradictory medical recommendations.Comment: 5 pages, 1 figure, accepted at 12th International Conference on Computational Semantics (IWCS-2017

LT3: sentiment analysis of figurative tweets: piece of cake #NotReally

This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We considered two approaches, classification and regression, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor. To this end, we combined a variety of standard lexical and syntactic features with specific features for capturing figurative content. All experiments were done using supervised learning with LIBSVM. For both runs, our system ranked fourth among fifteen submissions