文章基本信息

标题：Stacking Approach to Temporal Relation Classification with Temporal Inference
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作者：Natsuda Laokulrat ; Makoto Miwa ; Yoshimasa Tsuruoka 等
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2016
卷号：11
页码：53-78
DOI：10.11185/imt.11.53
出版社：Information and Media Technologies Editorial Board
摘要：Traditional machine-learning-based approaches to temporal relation classification use only local features, i.e., those relating to a specific pair of temporal entities (events and temporal expressions), and thus fail to incorporate useful information that could be inferred from nearby entities. In this paper, we use timegraphs and stacked learning to perform temporal inference for classification in the temporal relation classification task. In our model, we predict a temporal relation by considering the consistency of possible relations between nearby entities. Performing 10-fold cross-validation on the Timebank corpus, we achieve an F1 score of 60.25% using a graph-based evaluation, which is 0.90 percentage points higher than that of the local approach, outperforming other proposed systems.
关键词：Temporal Relation Classification;Information Extraction;Stacked Learning;Temporal Inference