Cross-Document Event Ordering through Temporal Relation Inference and Distributional Semantic Models
This paper focuses on the contribution of temporal relations inference and distributional semantic models to the event ordering task. Our system automatically builds ordered timelines of events from different written texts in English by performing first temporal clustering and then semantic clustering. In order to determine temporal compatibility, an inference from the temporal relationships between events –automatically extracted from a Temporal Information Processing system– is applied. Regarding semantic compatibility between events, we analyze two different distributional semantic models: LDA Topic modeling and Word2Vec word embeddings. Both semantic models together with the temporal inference have been evaluated within the framework of SemEval 2015 Task 4 Track B. Experiments show that, using both models, the current State of the Art is improved, showing significant advance in the Cross-Document Event Ordering task.
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