Mining activation force defined dependency patterns for relation extraction

Relation extraction is essential for most text mining tasks. Existing approaches on relation extraction are generally based on bootstrapping methodology which implies semantic drift problem. This paper presents a new approach to learn semantic dependency patterns, which can significantly alleviate this problem. To this end, a unique representation of activation force defined dependency pattern is presented. It is a trigger word mediated relation between an entity and its attribute value, and the trigger word is extracted by using the statistics of word activation forces between those words. The adaptability and the scalability of the framework are facilitated by the recursive and compositional bootstrap learning of patterns and seed pairs. To obtain insights of the reliability and applicability of the method, we applied it to the English Slot Filling task of Knowledge Base Population track at Text Analysis Conference 2013. Experimental results show that the proposed method has good performance in the implementation of English Slot Filling 2013 with the overall F1 value significantly higher than the best automatic result reported. The experimental results also demonstrate that the activation force based trigger word mining method plays an essential role in improving the performance.