![]() ![]() ![]() To tackle this aspect, we take the character identification corpus from the SemEval 2018 shared task that consists of entity annotation for singular mentions, and expand it by adding annotation for plural mentions. Unlike singular mentions each of which represents one entity, plural mentions stand for multiple entities. ![]() This paper analyzes arguably the most challenging yet under-explored aspect of resolution tasks such as coreference resolution and entity linking, that is the resolution of plural mentions. Finally, we identify the limitations of the existing approaches and the most promising perspectives. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We first describe the extraction process in a generic way and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. ![]() This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. However, works of fiction possess specific properties that make these tasks harder. Character networks are particularly relevant when considering works of fiction (e.g., novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. A character network is a graph extracted from a narrative in which vertices represent characters and edges correspond to interactions between them. ![]()
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