THU QUANTA at TAC 2009 KBP and RTE Track

Abstract

Population (KBP) track, and the Recognizing Textual Entailment (RTE) track. For the KBP track, we investigate two ranking strategies for Entity Linking task. We employ a Listwise “Learning to Rank ” model and Augmenting Naïve Bayes model to rank the candidate. We try to use learned patterns to solve the Slot Filling task. For the RTE track, we propose an interesting method, SEGraph (Semantic Ele-ments based Graph). This method divides the Hypothesis and Text into two types of seman-tic elements: Entity Semantic Element and Re-lation Semantic Element. The SEGraph is then constructed, with Entity Elements as nodes, and Relation Elements as edges for both Text and Hypothesis. Finally we recognize the tex-tual entailment based on the SEGraph of Text and SEGraph of Hypothesis. The evaluation results show that our proposed two frame-works are very effective for KBP and RTE tasks, respectively. 1