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• W2124898653 abstract "Recognizing textual entailment (TE) is a complex task involving knowledge from many different sources. One major source of information in this task is event factuality, since the inferences derivable from factual eventualities are different from those judged as possible or as non-existent. Some TE systems already factor in factuality features at the local level, but determining the factuality of events more generally involves dealing with information that is nonlocal to a particular textual event. In this paper, we present a tool providing events with their factuality values, characterized as pairs of modality and polarity features. In previous work, we identified polarity and modality at the local context with a performance of 92% precision and 56% recall. The research presented here extends and enhances our algorithm to incorporate the influence of non-local context as well as the identification of sources." @default.
• W2124898653 created "2016-06-24" @default.
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• W2124898653 date "2007-09-01" @default.
• W2124898653 modified "2023-10-09" @default.
• W2124898653 title "Semantic Analysis for Topical Segmentation of Videos" @default.
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• W2124898653 doi "https://doi.org/10.1109/icsc.2007.31" @default.
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