Integrating Word Embedding Offsets into the Espresso System for Part-Whole Relation Extraction

Part-whole relation, ormeronymy plays an important role in many domains. Among approaches to addressing the part-whole relation extraction task, the Espresso bootstrapping algorithm has proved to be effective by significantly improving recall while keeping high precision. In this paper, we first investigate the effect of using fine-grained subtypes and careful seed selection step on the performance of extracting part-whole relation. Our multitask learning and careful seed selection were major factors for achieving higher precision. Then, we improve the Espresso bootstrapping algorithm for part-whole relation extraction task by integrating word embedding approach into its iterations. The key idea of our approach is utilizing an additional ranker component, namely Similarity Ranker in the Instances Extraction phase of the Espresso system. This ranker component uses embedding offset information between instance pairs of part-whole relation. The experiments show that our proposed system achieved a precision of 84.9% for harvesting instances of the partwhole relation, and outperformed the original Espresso system.