Improving patient health literacy in imaging departments

Health Literacy is a key issue in the healthcare and its operational definition developed for the National Library of Medicine mention that is the “the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions” 1. Health literacy skills are needed for dialogue and discussion, reading health information, making decisions about participating in research activities, using medical tools for personal or familial health care, and even for voting on health or environmental...info:eu-repo/semantics/publishedVersio

Distributional Formal Semantics

Natural language semantics has recently sought to combine the complementary strengths of formal and distributional approaches to meaning. More specifically, proposals have been put forward to augment formal semantic machinery with distributional meaning representations, thereby introducing the notion of semantic similarity into formal semantics, or to define distributional systems that aim to incorporate formal notions such as entailment and compositionality. However, given the fundamentally different 'representational currency' underlying formal and distributional approaches - models of the world versus linguistic co-occurrence - their unification has proven extremely difficult. Here, we define a Distributional Formal Semantics that integrates distributionality into a formal semantic system on the level of formal models. This approach offers probabilistic, distributed meaning representations that are also inherently compositional, and that naturally capture fundamental semantic notions such as quantification and entailment. Furthermore, we show how the probabilistic nature of these representations allows for probabilistic inference, and how the information-theoretic notion of "information" (measured in terms of Entropy and Surprisal) naturally follows from it. Finally, we illustrate how meaning representations can be derived incrementally from linguistic input using a recurrent neural network model, and how the resultant incremental semantic construction procedure intuitively captures key semantic phenomena, including negation, presupposition, and anaphoricity.Comment: To appear in: Information and Computation (WoLLIC 2019 Special Issue

Quantum entanglement analysis based on abstract interpretation

Entanglement is a non local property of quantum states which has no classical counterpart and plays a decisive role in quantum information theory. Several protocols, like the teleportation, are based on quantum entangled states. Moreover, any quantum algorithm which does not create entanglement can be efficiently simulated on a classical computer. The exact role of the entanglement is nevertheless not well understood. Since an exact analysis of entanglement evolution induces an exponential slowdown, we consider approximative analysis based on the framework of abstract interpretation. In this paper, a concrete quantum semantics based on superoperators is associated with a simple quantum programming language. The representation of entanglement, i.e. the design of the abstract domain is a key issue. A representation of entanglement as a partition of the memory is chosen. An abstract semantics is introduced, and the soundness of the approximation is proven.Comment: 13 page

Extracting semantics for information extraction

Text documents are one of the means to store information.These documents can be found on personal desktop computers, intranets and in the Web. Thus the valuable knowledge is embedded in an unstructured form. Having an automated system that can extract information from the texts is very desirable.However, the major challenging issue in developing such an automated system is a natural language is not free from ambiguity and uncertainty problems.Thus semantic extraction remains a challenging task to researchers in this area.In this paper, a new framework to extract semantics for information extraction is proposed, where possibility theory, fuzzy sets, and knowledge about the subject and preceding sentence have been used as the key in resolving the ambiguity and uncertainty problems