Frame determinism of artistic narrative

The article presents the results of a study undertaken to study the nature and degree of determinism of the plot semantics of artistic narrative by frame structures that serve as the basis of this narrative. The research focused on questions about the properties of frames that predetermine the deployment of narrative (determinant frames); questions about the nature of the connection between plot-forming frames (interframe transitions); questions about the nature, perspective and degree of imperativeness of the constraints imposed by the determinant frames on the deployment of the narrative. It is concluded that the frame as a meaningful unit of the meaning of the text has prospective semantics, from which it follows that any frame that forms the main storyline in the narrative prescribes each functional component of its composition, first of all, the first and second terms, predicates and basic sirconstrants, defined trajectories of further existence in the plot. The assumption is confirmed that the organization of artistic narrative is determined by both superframes-determinants and superstructures that form thematic sequences of macrostructures. The experience of constructing a graphical two-level model of the frame semantics of artistic narrative is presented, the comparison of this model with the representation of the frame in the FrameNet system is carried out. It was also concluded that the systematic and consistent description of the plot-content semantics of the narrative as a kind of literary text requires the same consistent and systematic description of the semantic-syntactic frames that form this semantics. In the systematic description of the frame semantics of narrative, special attention should be paid to predicates of nuclear propositions, since it is their semantic valences that determine the filling of the terminals of semantic-syntactic frames and their description gives explanatory power to the description of the semantic structure of frames. The authors see the prospect of the research in the systematic description of the frame semantics of the narrative with the involvement of the resources of the FrameBank project.

H.761 Support of a New concept Element and a New "recognition" Node-Event to Enable Deep Learning-based Analyses for Media-Nodes

Machine Learning field, methods based on Deep Learning (e.g. CNN, RNN) becomes the state-of-the-art in several problems of the multimedia domain, especially in audio-visual tasks. Typically, the training of Deep Learning Methods is done in a supervised manner, and it is trained on datasets containing thousands/millions of media examples and several related concepts/classes. During training, the Deep Learning Methods learn a hierarchy of filters that are applied to input data to classify/recognize the media content. In computer vision scenario, for example, given image pixels, the series of layers of the network can learn to extract visual features from it, the shallow layers can extract lower-level features (e.g. edges, corner, contours), while the deeper combine these features to produce higher-level features (e.g. textures, part of objects). These representative features can be clustered into groups, each one representing a specific concept. H.761 NCL currently lacks support for Deep Learning Methods inside their application specification. Because those languages still focus on presentations tasks such as capture, streaming, and presentation. They do not consider programmers to describe the semantic understanding of the used media and handle recognition of such under-standing. In this proposal, we aim at extending NCL to provide such support. More precisely, our proposal able NCL application support: (1) describe learning-based on structured multimedia datasets; (2) recognize content semantics of the media elements in presentation time. To achieve such goals, we propose, an extension that includes: (a) the new "knowledge" element describe concepts based on multimedia datasets; (b) "area" anchor with an associated "recognition" event that describes when a concept occurrences in multimedia content.

DEDSC: A Domain Expert Discovery Method Based on Structure and Content

Researchers usually extract domain experts only through analyzing network structure or partitioning users into several communities according to their label information. Combining structure and content to discovery domain experts is a new attempt. Motivated by that, this paper proposes a domain expert discovery method based on network structure and content semantics, called DEDSC, which can extract authority nodes in overlapping communities. To analyze the overall authority for each user in the social network, two definitions, structure authority value and content authority value, are proposed to evaluate the authority of users in different perspectives. Partitioning users into communities can make the results more accurate. Experimental results show that our proposed method can discover domain experts effectively. In addition, when we need to extract domain experts in a new test dataset, we do not need to re-train the data in the training dataset.