The visualization of procedural knowledge from textual documents using 3D animation may be a way to improve understanding. We are interested in applying this approach to documents relating to patient education for bariatric surgery: a domain with challenging textual documents describing behavior recommendations that contain few procedural steps and leave much commonsense knowledge unspecified. In this work we look at how to automatically capture knowledge from a range of differently phrased recommendations and use that with implicit knowledge about compliance and violation, such that the recommendations can be visualized using 3D animations. Our solution is an end-to-end system that automates this process via: analysis of input recommendations to uncover their conditional structure; the use of commonsense knowledge and deontic logic to generate compliance and violation rules; and mapping of this knowledge to update a default knowledge base, which is used to generate appropriate sequences of visualizations. In this paper we overview this approach and demonstrate its potential.

In this work, we revisit Shared Task 1 from the 2012 *SEM Conference: the au-tomated analysis of negation. Unlike the vast majority of participating systems in 2012, our approach works over explicit and formal representations of proposi-tional semantics, i.e. derives the notion of negation scope assumed in this task from the structure of logical-form meaning rep-resentations. We relate the task-specific interpretation of (negation) scope to the concept of (quantifier and operator) scope in mainstream underspecified semantics. With reference to an explicit encoding of semantic predicate-argument structure, we can operationalize the annotation deci-sions made for the 2012 *SEM task, and demonstrate how a comparatively simple system for negation scope resolution can be built from an off-the-shelf deep parsing system. In a system combination setting, our approach improves over the best pub-lished results on this task to date. 1

This article explores a combination of deep and shallow approaches to the problem of resolving the scope of speculation and negation within a sentence, specifically in the domain of biomedical research literature. The first part of the article focuses on speculation. After first showing how speculation cues can be accurately identified using a very simple classifier informed only by local lexical context, we go on to explore two different syntactic approaches to resolving the in-sentence scopes of these cues. Whereas one uses manually crafted rules operating over dependency structures, the other automatically learns a discriminative ranking function over nodes in constituent trees. We provide an in-depth error analysis and discussion of various linguistic properties characterizing the problem, and show that although both approaches perform well in isolation, even better results can be obtained by combining them, yielding the best published results to date on the CoNLL-2010 Shared Task data. The last part of the article describes how our speculation system is ported to also resolve the scope of negation. With only modest modifications to the initial design, the system obtains state-of-the-art results on this task also.