Jointly Identifying Temporal Relations with Markov Logic

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 405–413, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Jointly Identifying Temporal Relations with Markov Logic Katsumasa Yoshikawa NAIST, Japan katsumasa-y@is.naist.jp Sebastian Riedel University of Tokyo, Japan sebastian.riedel@gmail.com Masayuki Asahara NAIST, Japan masayu-a@is.naist.jp Yuji Matsumoto NAIST, Japan matsu@is.naist.jp Abstract Recent work on temporal relation iden- tification has focused on three types of relations between events: temporal rela- tions between an event and a time expres- sion, 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 ne- glects logical constraints between tempo- ral relations of different types that we be- lieve to be helpful. We therefore propose a Markov Logic model that jointly identifies relations of all three relation types simul- taneously. 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 or- dering) 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 an- notated with temporal information, it became pos- sible to apply machine learning to temporal order- ing (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 ques- tion answering and machine translation. Most of these approaches follow a simple schema: they learn classifiers that predict the tem- poral order of a given event pair based on a set of the pair’s of features. This approach is local in the sense that only a single temporal relation is consid- ered at a time. Learning to predict temporal relations in this iso- lated manner has at least two advantages over any approach that considers several temporal relations jointly. First, it allows us to use off-the-shelf ma- chine learning software that, up until now, has been mostly focused on the case of local classifiers. Sec- ond, it is computationally very efficient both in terms of training and testing. However, the local approach has a inherent drawback: it can lead to solutions that violate logi- cal constraints we know to hold for any sets of tem- poral relations. For example, by classifying tempo- ral relations in isolation we may predict that event A happened before, and event B after, the time of document creation, but also that event A hap- pened after event B—a clear contradiction in terms of temporal logic. In order to repair the contradictions that the local classifier …

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