TitleEvent-based time label propagation for automatic dating of news articles
AuthorsGe, Tao
Chang, Baobao
Li, Sujian
Sui, Zhifang
AffiliationKey Laboratory of Computational Linguistics, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing, China
Issue Date2013
Citation2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013.Seattle, WA, United states.
AbstractSince many applications such as timeline summaries and temporal IR involving temporal analysis rely on document timestamps, the task of automatic dating of documents has been increasingly important. Instead of using feature-based methods as conventional models, our method attempts to date documents in a year level by exploiting relative temporal relations between documents and events, which are very effective for dating documents. Based on this intuition, we proposed an event-based time label propagation model called confidence boosting in which time label information can be propagated between documents and events on a bipartite graph. The experiments show that our event-based propagation model can predict document timestamps in high accuracy and the model combined with a MaxEnt classifier outperforms the state-of-the-art method for this task especially when the size of the training set is small. ? 2013 Association for Computational Linguistics.
Appears in Collections:信息科学技术学院

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