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In this paper, we introduce the CrowdTruth open-source software framework for machine-human computation, that implements a novel approach to gathering human annotation data in a wide range of annotation tasks and on a variety of media (e.g. text, images, videos). The CrowdTruth approach captures human semantics through a pipeline of three processes: a)(More)
Using crowdsourcing platforms such as CrowdFlower and Amazon Mechanical Turk for gathering human annotation data has become now a mainstream process. Such crowd involvement can reduce the time needed for solving an annotation task and with the large number of annotators can be a valuable source of annotation diversity. In order to harness this diversity(More)
A viewpoint is a triple consisting of an entity, a topic related to this entity and sentiment towards this topic. In time-aware multi-viewpoint summarization one monitors viewpoints for a running topic and selects a small set of informative documents. In this paper, we focus on time-aware multi-viewpoint summarization of multilingual social text streams.(More)
DIVE is a linked-data digital cultural heritage collection browser. It was developed to provide innovative access to heritage objects from heterogeneous collections, using historical events and narratives as the context for searching, browsing and presentating of individual and group of objects. This paper describes the DIVE Web Demonstrator. This(More)
When crowdsourcing gold standards for NLP tasks, the workers may not reach a consensus on a single correct solution for each task. The goal of Crowd Truth is to embrace such disagreement between individual annotators and harness it as useful information to signal vague or ambiguous examples. Even though the technique relies on disagreement, we also assume(More)