A Bottom-Up Kernel of Pattern Learning for Relation Extraction

Measuring the similarity of patterns is the key in pattern-based approaches in relation extraction. Most existing methods generally rely on inflexible pattern similarity measurements which often lead to low recall. In this work, a novel kernel-based model is proposed to address this problem. Depending on the pattern similarities produced by our bottom-up kernel, the most similar semantic shortest dependency patterns are selected to update seed patterns in each iteration of bootstrapping. To obtain insights of the reliability and applicability of our proposed method, we applied it to the task of English Slot Filling (ESF) in Knowledge Base Population (KBP) track at Text Analysis Conference (TAC). The experimental results validate our proposed method that importantly improves the recall which resulting in the improvement of F1 value. The effectiveness of the bottom-up kernel is also verified by further experimental results.