tailieunhanh - Báo cáo khoa học: "Jointly Identifying Temporal Relations with Markov Logic"

Recent work on temporal relation identification has focused on three types of relations between events: temporal relations between an event and a time expression, between a pair of events and between an event and the document creation time. These types of relations have mostly been identified in isolation by event pairwise comparison. However, this approach neglects logical constraints between temporal relations of different types that we believe to be helpful. We therefore propose a Markov Logic model that jointly identifies relations of all three relation types simultaneously. . | Jointly Identifying Temporal Relations with Markov Logic Katsumasa Yoshikawa Sebastian Riedel Masayuki Asahara NAIST Japan University of Tokyo Japan NAIST Japan katsumasa-y@ masayu-a@ Yuji Matsumoto NAIST Japan matsu@ Abstract Recent work on temporal relation identification has focused on three types of relations between events temporal relations between an event and a time expression between a pair of events and between an event and the document creation time. These types of relations have mostly been identified in isolation by event pairwise comparison. However this approach neglects logical constraints between temporal relations of different types that we believe to be helpful. We therefore propose a Markov Logic model that jointly identifies relations of all three relation types simultaneously. By evaluating our model on the TempEval data we show that this approach leads to about 2 higher accuracy for all three types of relations and to the best results for the task when compared to those of other machine learning based systems. 1 Introduction Temporal relation identification or temporal ordering involves the prediction of temporal order between events and or time expressions mentioned in text as well as the relation between events in a document and the time at which the document was created. With the introduction of the TimeBank corpus Pustejovsky et al. 2003 a set of documents annotated with temporal information it became possible to apply machine learning to temporal ordering Boguraev and Ando 2005 Mani et al. 2006 . These tasks have been regarded as essential for complete document understanding and are useful for a wide range of NLP applications such as question answering and machine translation. Most of these approaches follow a simple schema they learn classifiers that predict the temporal order of a given event pair based on a set of the pair s of features. This approach is local in the sense .