CUNY BLENDER TAC-KBP2011 Temporal Slot Filling System Description

In this paper we describe the participation of the CUNY-BLENDER team in the Temporal Slot Filling (TSF) pilot task organized as part of the TAC-KBP2010 evaluation. Our team submitted results for both the “diagnostic” and “full” TSF subtasks, obtaining the top score in the diagnostic subtask. We implemented a “structured” and a “flat” approach to the classification of temporal expressions. The structured approach captures long syntactic contexts surrounding the query entity, slot fill and temporal expression using a dependency path kernel tailored to this task. The flat approach exploits information such as the lexical context and shallow dependency features. In order to provide enough training data for these classifiers we used a distant supervision approach to automatically generate a large amount of training instances from the Web. This data was further refined by applying logistic regression models for instance relabeling and feature selection methods.