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- W4289667044 abstract "The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results." @default.
- W4289667044 created "2022-08-03" @default.
- W4289667044 creator A5031111983 @default.
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- W4289667044 date "2018-08-28" @default.
- W4289667044 modified "2023-09-25" @default.
- W4289667044 title "Temporal Information Extraction by Predicting Relative Time-lines" @default.
- W4289667044 doi "https://doi.org/10.48550/arxiv.1808.09401" @default.
- W4289667044 hasPublicationYear "2018" @default.
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