The focus of this paper is on how events can be detected & extracted
from natural language text, and how those are represented for use on the semantic web. We draw an inspiration from the similarity between crowdsourcing approaches for tagging and text annotation task for ground truth of events. Thus,
we propose a novel approach that harnesses the disagreement between the human annotators by deﬁning a framework to capture and analyze the nature of the
disagreement. We expect two novel results from this approach. On the one hand,
achieving a new way of measuring ground truth (performance), and on the other
hand identifying a new set of semantic features for learning in event extraction.
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