This paper describes the systems of THU QUANTA in Text Analysis Conference (TAC) 2009. We participated in the Knowledge Base 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 Naive 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 Elements based Graph). This method divides the Hypothesis and Text into two types of semantic elements: Entity Semantic Element and Relation 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 textual entailment based on the SEGraph of Text and SEGraph of Hypothesis. The evaluation results show that our proposed two frameworks are very effective for KBP and RTE tasks, respectively.
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